@article{BMJOpen1,

title = {Exploring decision-makers’ challenges and strategies when selecting multiple systematic reviews: insights for AI decision support tools in healthcare},

author = {Carole Lunny and Sera Whitelaw and Emma K Reid and Yuan Chi and Nicola Ferri and Jia He (Janet) Zhang and Dawid Pieper and Salmaan Kanji and Areti-Angeliki Veroniki and Beverley Shea and Jasmeen Dourka and Clare Ardern and Ba Pham and Ebrahim Bagheri and Andrea C Tricco},

doi = {10.1136/bmjopen-2024-084124},

year  = {2024},

date = {2024-07-07},

urldate = {2024-07-07},

journal = {BMJ Open},

volume = {14},

pages = {e084124},

abstract = {Background Systematic reviews (SRs) are being published at an accelerated rate. Decision-makers may struggle with comparing and choosing between multiple SRs on the same topic. We aimed to understand how healthcare decision-makers (eg, practitioners, policymakers, researchers) use SRs to inform decision-making and to explore the potential role of a proposed artificial intelligence (AI) tool to assist in critical appraisal and choosing among SRs.



Methods We developed a survey with 21 open and closed questions. We followed a knowledge translation plan to disseminate the survey through social media and professional networks.



Results Our survey response rate was lower than expected (7.9% of distributed emails). Of the 684 respondents, 58.2% identified as researchers, 37.1% as practitioners, 19.2% as students and 13.5% as policymakers. Respondents frequently sought out SRs (97.1%) as a source of evidence to inform decision-making. They frequently (97.9%) found more than one SR on a given topic of interest to them. Just over half (50.8%) struggled to choose the most trustworthy SR among multiple. These difficulties related to lack of time (55.2%), or difficulties comparing due to varying methodological quality of SRs (54.2%), differences in results and conclusions (49.7%) or variation in the included studies (44.6%). Respondents compared SRs based on the relevance to their question of interest, methodological quality, and recency of the SR search. Most respondents (87.0%) were interested in an AI tool to help appraise and compare SRs.



Conclusions Given the identified barriers of using SR evidence, an AI tool to facilitate comparison of the relevance of SRs, the search and methodological quality, could help users efficiently choose among SRs and make healthcare decisions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MLJ2024-1,

title = {Gender Disentangled Representation Learning in Neural Rankers},

author = {Shirin Seyedsalehi and Sara Salamat and Negar Arabzadeh and Sajad Ebrahimi and Morteza Zihayat and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/shirin_mlj/},

year  = {2024},

date = {2024-09-09},

urldate = {2024-09-09},

journal = {Machine Learning Journal},

abstract = {Recent studies have demonstrated that while neural ranking methods excel in retrieval effectiveness, they also tend to amplify stereotypical biases, especially those related to gender. Current mitigation strategies often focus on adjusting training methods, like adversarial techniques or data balancing, but typically overlook explicit consideration of gender as an attribute. In this paper, we introduce a systematic approach that treats gender as a distinct component within neural ranker representations. Our neural disentanglement method separates content semantics from gender information, enabling the neural ranker to evaluate document relevance based on content alone, without the interference of gender-related information during retrieval. Our extensive experiments demonstrate that: (1) our disentanglement approach matches the effectiveness of baseline models and offers more consistent performance across queries of different gender affiliations; (2) isolating gender within the representations allows the neural ranker to produce an unbiased list of documents, not favoring any specific gender; and (3) the disentangled gender component effectively and concisely captures gender information independently from the semantic content.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IPM2024ab,

title = {Predicting Users’ Future Interests on Social Networks: A Reference Framework},

author = {Fattane Zarrinkalam and Havva Alizadeh Noughabi and Zeinab Noorian and Hossein Fani and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/predicting_users__future_interests_on_social_networks__a_reference_framework-3-4/},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-01},

journal = {Information Processing and Management },

abstract = {Predicting users’ interests on social networks is gaining attention due to its potential to cater customized information and services to the end users. Although previous works have extensively explored how users’ interests can be modeled on social networks, there has been limited investigation into the prediction of users’ future interests. The objective of our work in this paper is to empirically study the effectiveness of different sets of features based on users’ past social interactions, historical interests and their temporal dynamics to predict their interests over a collection of future-yet-unobserved topics. More specifically, we introduce and formalize the features for interest prediction in four categories: user-based, topical, explicit user-topic engagement, and friends’ influence. We further explore the influence of temporality by augmenting features with information pertaining to users’ historical interests and social connections. We model the task of future interest prediction as a learning-to-rank problem where different features and their related categories are ranked based on their relevance and performance in interest prediction, and investigate the efficiency of different features individually and comparatively for predicting the future interest of users with different activity levels in social networks over on unobserved topics. After conducting experiments on a real-world dataset sourced from Twitter, we have identified several noteworthy findings: 1) relevance feature in the category of past explicit user-topic engagement is the strongest indicator for predicting user’s future interest across all user groups, with an observed 8.57% decrease in NDCG and an 8.95% decrease in MAP when it is removed in the ablation study. 2) the observation of an 8.06% decrease in NDCG and a 7.3% decrease in MAP, when topical features such as popularity, freshness, and coherence are removed in the ablation study, highlights their significance as among the strongest indicators for users’ future interest, particularly for low-activity users. 3) although temporal features show a clear positive impact across user groups with varying levels of activity (resulting in a 4.5% decrease in NDCG and a 7.3% decrease in MAP when removed in the ablation study), the temporal topical features do not demonstrate a significant positive effect, and 4) The removal of user-specific characteristics such as influence and personality traits in the ablation study reveals their significant impact in predicting future interest over cold topics, reflected by a 5.49% decrease in NDCG and a 5.72% decrease in MAP. Our findings make significant contributions to the field of future interest prediction, offering valuable insights and practical implications for various applications in social network analysis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MLJ2024-2,

title = {Robust Query Performance Prediction for Dense Retrievers via Adaptive Disturbance Generation},

author = {Abbas Saleminezhad and Negar Arabzadeh and Radin Hamidi Rad and Soosan Beheshti and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/suren-mlj/},

year  = {2024},

date = {2024-09-09},

urldate = {2024-09-09},

journal = {Machine Learning Journal},

abstract = {This paper introduces​ ADG-QPP​ (Adaptive Disturbance Generation), an unsupervised Query Performance Prediction (QPP) method designed specifically for dense neural retrievers. The underlying foundation of ADG-QPP is to measure query performance based on its degree of robustness towards perturbations. Traditional QPP methods rely on predefined lexical perturbations on the query, which only apply to sparse retrieval methods and fail to maintain consistent performance across different datasets. In our work, we address these limitations by perturbing the query by injecting disturbance leveraged by the focal network-based measurements including node-based, edge-based and cluster-based metrics, into its neural embedding representation. Rather than applying the same perturbation across all queries, our approach develops an instance-wise disturbance for each query that is then used for its perturbation. Through extensive experiments on three benchmark datasets, we demonstrate that ADG-QPP outperforms state-of-the-art baselines in terms of Kendall tau, Spearman rho, and Pearson’s rho correlations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{JBI2023,

title = {A Self-supervised Language Model Selection Strategy for Biomedical Question Answering},

author = {Negar Arabzadeh and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/jbi_2023_arabzadeh/},

year  = {2023},

date = {2023-09-01},

urldate = {2023-09-01},

journal = {Journal of Biomedical Informatics},

abstract = {Large neural-based Pre-trained Language Models (PLM) have recently gained much attention due to their noteworthy performance in many downstream Information Retrieval (IR) and Natural Language Processing (NLP) tasks. PLMs can be categorized as either general-purpose, which are trained on resources such as large-scale Web corpora, and  domain-specific which are trained on in-domain or mixed-domain corpora. While domain-specific PLMs have shown promising performance on domain-specific tasks, they are significantly more computationally expensive compared to general-purpose PLMs as they have to be either retrained or trained from scratch. The objective of our work in this paper is to explore whether it would be possible to leverage general-purpose PLMs to show competitive performance to domain-specific PLMs without the need for expensive retraining of the PLMs for domain-specific tasks. By focusing specifically on the recent BioASQ Biomedical Question Answering task, we show how different general-purpose PLMs show synergistic behaviour in terms of performance, which can lead to overall notable performance improvement when used in tandem with each other. More concretely, given a set of  general-purpose PLMs, we propose a self-supervised method for training a classifier that systematically selects the PLM that is most likely to answer the question correctly on a per-input basis. We show that through such a selection strategy, the performance of general-purpose PLMs can become competitive with domain-specific PLMs while remaining computationally light since there is no need to retrain the large language model itself.  We run experiments on the BioASQ dataset, which is a large-scale biomedical question-answering benchmark. We show that utilizing our proposed selection strategy can show statistically significant performance improvements on general-purpose language models  with an average of 16.7% when using only lighter models such as DistilBERT and DistilRoBERTa, as well as 14.2% improvement when using relatively larger models such as BERT and RoBERTa and so, their performance become competitive with domain-specific large language models such as PubMedBERT.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{TOIS2023,

title = {A Variational Neural Architecture for Skill-based Team Formation},

author = {Radin Hamid Rad and Hossein Fani and Ebrahim Bagheri and Mehdi Kargar and Divesh Srivastava and Jaroslaw Szlichta},

url = {https://ls3.rnet.torontomu.ca/tois_2023_hamidrad/},

year  = {2023},

date = {2023-03-12},

urldate = {2023-03-12},

journal = {ACM Transactions on Information Systems},

abstract = {Team formation is concerned with the identification of a group of experts who have a high likelihood of

effectively collaborating with each other in order to satisfy a collection of input skills. Solutions to this task

have mainly adopted graph operations and at least have the following limitations: (1) they are computationally

demanding as they require finding shortest paths on large collaboration networks; (2) they use various types of

heuristics to reduce the exploration space over the collaboration network in order to become practically feasible;

therefore, their results are not necessarily optimal; and, (3) they are not well-suited for collaboration network

structures given the sparsity of these networks. Our work proposes a variational Bayesian neural network

architecture that learns representations for teams whose members have collaborated with each other in the past.

The learnt representations allow our proposed approach to mine teams that have a past collaborative history

and collectively cover the requested desirable set of skills. Through our experiments, we demonstrate that our

approach shows stronger performance compared to a range of strong team formation techniques from both

quantitative and qualitative perspectives.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {DyHNet: Learning Dynamic Heterogeneous Network Representations},

author = {Hoang Nguyen and Radin Hamidi Rad and Fattane Zarrinkalam and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/wp-content/uploads/2024/04/v1_covered.pdf},

doi = {https://doi.org/10.1016/j.ins.2023.119371},

year  = {2023},

date = {2023-07-05},

urldate = {2023-07-05},

journal = {Information Sciences},

abstract = {Many real-world networks, such as social networks, contain structural heterogeneity and experience temporal evolution. However, while there has been growing literature on network representation learning, only a few have addressed the need to learn representations for dynamic heterogeneous networks. The objective of our work in this paper is to introduce DyHNet, which learns representations for such networks and distinguishes itself from the state-of-the-art by systematically capturing (1) local node semantics, (2) global network semantics and (3) longer-range temporal associations between network snapshots when learning network representations. Through experiments on four real-world datasets from different domains, namely IMDB with 4,178 movies, AMiner with 10, 674 papers, Yelp with 2, 693 businesses, and DBLP with 14,376 papers, we demonstrate that our proposed method is able to show consistently better and more robust performance compared to the state-of-the-art techniques on link prediction and node classification tasks. More specifically, we are superior to the best baseline in the temporal link prediction task by approximately 13% and 15% on F1-score for the IMDB and AMiner datasets, respectively. Further, in the node classification task, our findings illustrate that the Micro F1 scores of our proposed model increase by 13% and 17% compared to the runner-up model on the

Yelp and DBLP datasets, respectively.},

key = {Dynamic heterogeneous network, Network representation learning, Random walk},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IRJ2023,

title = {Learning Heterogeneous Subgraph Representations for Team Discovery},

author = {Radin Hamidi Rad and Hoang Nguyen and Feras Al-Obeidat and Ebrahim Bagheri and Mehdi Kargar and Divesh Srivastava and Jaroslaw Szlichta and Fattane Zarrinkalam},

url = {https://ls3.rnet.torontomu.ca/irj_2023_hamidi/},

doi = {https://doi.org/10.1007/s10791-023-09421-6},

year  = {2023},

date = {2023-09-01},

urldate = {2023-09-01},

journal = {Information Retrieval Journal},

abstract = {The team discovery task is concerned with finding a group of experts from a collaboration network who would collectively cover a desirable set of skills. Most prior work for team discovery either adopt graph-based or neural mapping approaches. Graph-based approaches are computationally intractable often leading to sub-optimal team selection. Neural mapping approaches have better performance, however, are still limited as they learn individual representations for skills and

experts and are often prone to overfitting given the sparsity of collaboration networks. Thus, we define the team discovery task as one of learning subgraph representations from a heterogeneous collaboration network where the subgraphs represent teams which are then used to identify relevant teams for a given set of skills. As such, our approach captures local (node interactions with each team) and global (subgraph interactions between teams) characteristics of the representation network and allows us to easily map between any homogeneous and heterogeneous subgraphs in the network to effectively discover teams. Our experiments over two real-world datasets from different domains, namely DBLP bibliographic dataset with 10,647 papers and IMDB with 4,882 movies, illustrate that our approach outperforms the state-of-the-art baselines on a range of ranking and quality metrics. More

specifically, in terms of ranking metrics, we are superior to the best baseline by approximately 15% on the DBLP dataset and by approximately 20% on the IMDB dataset.  Further, our findings illustrate that our approach consistently shows a robust performance improvement over the baselines.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{InS2023M,

title = {Learning to Rank and Predict: Multi-Task Learning for Ad hoc Retrieval and Query Performance Prediction},

author = {Maryam Khodabakhsh and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/is_2023_qpp_khodabakhsh/},

doi = {https://doi.org/10.1016/j.ins.2023.119015},

year  = {2023},

date = {2023-04-22},

urldate = {2023-04-22},

journal = {Information Sciences},

abstract = {The ad hoc retrieval task aims at ranking relevant documents to a user query such that the most relevant documents are ranked higher compared to less relevant ones. Given the performance of the ad hoc retrieval task can vary across a range of queries, researchers have extensively explored the interrelated task of query performance prediction, which aims at estimating the quality of the search results for a user query without having access to relevance judgments. Traditionally and to-date, the two tasks have been explored as separate tasks where ad hoc retrieval and query performance prediction have been performed in isolation. In this paper, we propose to learn joint tasks that would perform ad hoc retrieval and at the same time predict the quality of the produced rankings. More specifically, we propose a mutli-task learning approach, called Multi-task Query Performance Prediction Framework (M-QPPF), which learns document ranking and query performance prediction tasks simultaneously. In M-QPPF, we adopt a shared BERT layer, which is fine-tuned to learn representations for query- document pairs in the embedding space such that the representations effectively encode the cross-interaction between the query and documents. We perform comprehensive experiments against state-of-the-art methods over large scale datasets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cscw2022,

title = { Exploring the Utility of Social Content for Understanding Future In-Demand Skills },

author = {Jalehsadat Mahdavimoghaddam and Ayush Bahuguna and Ebrahim Bagheri },

url = {https://ls3.rnet.torontomu.ca/3555114_compressed/},

doi = {https://doi.org/10.1145/3555114},

year  = {2022},

date = {2022-03-30},

urldate = {2022-03-30},

journal = {Proc. ACM Hum. Comput. Interact. (CSCW)},

abstract = { Rapid technological innovations, especially in the Information Technology space, demand the workforce to be vigilant by acquiring new skills to remain relevant and employable. The workforce needs to be engaged in a continuous lifelong learning process by educating themselves about skills that will be in-demand in the future. To do so, it is important for students, job seekers, and even recruiters to know which skills will be in demand in the future to invest time and resources in these skills. On this basis, the main objective of this paper is to investigate whether social content can offer insight into potential future in-demand skills in the IT job market. Based on the analysis of social content from Reddit and Job Posting data from Dice and Monster websites, we find that social content related to job skills are strong indicators for future in-demand skills. We further find that specific social content associated with recruitment-related topics are stronger indicators of future skills. Our findings encourage learners and job seekers to pay close attention to online social content to forward plan new skills and maximize their employability.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2020-hawre,

title = {A Systemic Functional Linguistics Approach to Implicit Entity Recognition in Tweets},

author = {Hawre Hosseini and Mehran Mansouri and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0306457322000772-main/},

doi = {https://doi.org/10.1016/j.ipm.2022.102957},

year  = {2022},

date = {2022-04-23},

urldate = {2022-04-23},

journal = {Information Processing and Management},

volume = {59},

number = {102957},

issue = {4},

abstract = {The identification of knowledge graph entity mentions in textual content has already attracted much attention. The major assumption of existing work is that entities are explicitly mentioned in text and would only need to be disambiguated and linked. However, this assumption does not necessarily hold for social content where a significant portion of information is implied. The focus of our work in this paper is to identify whether textual social content include implicit mentions of knowledge graph entities or not, hence forming a two-class classification problem. To this end, we adopt the systemic functional linguistic framework that allows for capturing meaning expressed through language. Based on this theoretical framework we systematically introduce two classes of features, namely syntagmatic and paradigmatic features, for implicit entity recognition. In our experiments, we show the utility of these features for the task, report on ablation studies, measure the impact of each feature subset on each other and also provide a detailed error analysis of our technique.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Etemadi2022,

title = {Embedding-based Team Formation for Community Question Answering},

author = {Roohollah Etemadi and Morteza Zihayat and Kuan Feng and Jason Adelman and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0020025522010829-main/},

doi = {https://doi.org/10.1016/j.ins.2022.09.036},

year  = {2022},

date = {2022-09-09},

urldate = {2022-09-09},

journal = {Information Sciences},

abstract = {Finding a qualified individual who can independently answer a question on a community question answering platform is becoming more challenging due to the increasing multidisciplinary nature of posted questions. As such, finding a group of experts to  collaboratively answer the questions is of paramount importance. To this end, our proposed method forms teams of experts who can collectively answer new questions. Our approach, called team2box, learns neural embedding representations based on the content of the posted questions, experts’ engagement with these questions, and past expert collaboration history in order to form a team to answer the posted question. It embeds experts and questions as points and existing teams as regions within the embedding space. Such an approach allows team2box to form a team whose members (1) collectively cover the knowledge required to answer a question, (2) have successful past experience in jointly answering similar questions, and (3) can work efficiently together to answer the question. Extensive experiments on real-life datasets from Stack Exchange show that team2box outperforms the state-of-the-art by discovering teams with on average 38.97% more covering the skills required to answer new questions and employing experts with collectively a high expertise level. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{INS-2022b,

title = {Feature-based Question Routing in Community Question Answering Platforms },

author = {Soroosh Sorkhani and Amin Bigdeli and Roohollah Etemadi and Morteza Zihayat and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0020025522006661-main_compressed/},

doi = {https://doi.org/10.1016/j.ins.2022.06.072},

year  = {2022},

date = {2022-06-19},

urldate = {2022-06-19},

journal = {Information Sciences},

abstract = {Community question answering (CQA) platforms are receiving increased attention and are becoming an indispensable source of information in different domains ranging from board games to physics. The success of these platforms dependent on how efficiently new questions are assigned to community experts, known as called question routing. In this paper, we address the problem of question routing by adopting a learning to rank approach over five CQA websites in the context of which we introduce 74 features and systematically classify them into content-based and social-based categories. Our extensive experiments on datasets from five real online question answering websites indicate that content-based features related to tags and topics as well as social features that are related to user characteristics and user temporality are effective for question routing. Our work shows the ability to improve performance compared to the state-of-the-art neural matchmaking methods that lack the interpretability offered by our work. The improvement can be as high as on average 2.74% and20 2.27% in terms of common ranking metrics, Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP) respectively, compared to our best baselines.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{INS-2022c,

title = {Preventing Proof-of-Work Mining Attacks},

author = {Hamid Azimy and Ali A. Ghorbani and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0020025522007241-main/},

doi = {https://doi.org/10.1016/j.ins.2022.07.035},

year  = {2022},

date = {2022-07-07},

urldate = {2022-07-07},

journal = {Information Sciences},

abstract = {Bitcoin mining is the process of generating new blocks in the Bitcoin blockchain. This process is vulnerable to different types of attacks. One of the most famous attacks in this category is selfish mining. This attack is essentially a strategy that a sufficiently powerful mining pool can follow to obtain more revenue than its fair share. The reason that selfish mining is effective is the difficulty adjustment algorithm used in the Bitcoin network. In this paper, we analyze the profitability of selfish mining with respect to time and propose an alternative difficulty adjustment algorithm that discourages selfish mining while allowing the Bitcoin network to remain scalable. We analyze our proposed solution, present the results, and discuss its effectiveness. Based on our analysis, our proposed algorithm effectively increases the profitability waiting time for the attackers to almost double its original value. For example, for a miner with 40% of the network's hash power, the algorithm extends the waiting time from 4 weeks to more than 11 weeks. This will discourage attackers from performing their malicious activities. We also show that our proposed algorithm allows the network to scale while it increases the waiting time.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{INS-2022a,

title = {Qualitative Measures for Ad hoc Table Retrieval},

author = {Maryam Khodabakhsh and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0020025522005126-main/},

doi = {https://doi.org/10.1016/j.ins.2022.05.080},

year  = {2022},

date = {2022-05-23},

urldate = {2022-05-23},

journal = {Information Sciences},

abstract = {The focus of our work is the ad hoc table retrieval task, which aims to rank a list of structured tabular objects in response to a user query. Given the importance of this task, various methods have already been proposed in the literature that focus on syntactic, semantic and neural representations of tables for determining table relevance. However, recent works have highlighted queries that are consistently difficult for baseline methods to satisfy, referred to as hard queries. For this reason, the objectives of this paper include: (1) effectively satisfying hard queries by proposing three classes of qualitative measures, namely coherence, interpretability and exactness, (2) offering a systematic approach to interpolate these three classes of measures with each other and with baseline table retrieval methods, and (3) performing extensive experiments using a range of baseline retrieval methods to show the feasibility of the proposed measures for hard queries. We demonstrate that the consideration of the proposed qualitative measures will lead to improved performance for hard queries on a range of state-of-the-art ad hoc table retrieval baselines. We further show that our proposed measures are synergistic and will lead to even higher performance improvements over the baselines when interpolated with each other. The improvements measure up to 22.94% on the Semantic Table Retrieval (STR) method with an NDCG@20 of 0.5, which is superior to the performance of any state-of-the-art baseline for hard queries in the ad hoc table retrieval task.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mirlohi2022,

title = {Social Alignment Contagion in Online Social Networks},

author = {Amin Mirlohi and Jalehsadat Mahdavimoghaddam and Jelena Jovanovic and Feras Al-Obeidat and Ali A Ghorbani and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/social_alignment_contagion_in_online_social_networks_compressed/},

doi = {https://doi.org/10.1109/TCSS.2022.3226346},

year  = {2022},

date = {2022-11-29},

urldate = {2022-11-29},

journal = {IEEE Transactions on Computational Social Systems},

abstract = {Researchers have already observed social contagion effects in both in-person and online interactions. However, such studies have primarily focused on users’ beliefs, mental states, and interests. In this article, we expand the state of the art by exploring the impact of social contagion on social alignment, i.e., whether the decision to socially align oneself with the general opinion of the users on the social network is contagious to one’s connections on the network or not. The novelty of our work in this article includes: 1) unlike earlier work, this article is among the first to explore the contagiousness of the concept of social alignment on social networks; 2) our work adopts an instrumental variable approach to determine reliable causal relations between observed social contagion effects on the social network; and 3) our work expands beyond the mere presence of contagion in social alignment and also explores the role of population heterogeneity on social alignment contagion. Based on the systematic collection and analysis of data from two large social network platforms, namely, Twitter and Foursquare, we find that a user’s decision to socially align or distance from social topics and sentiments influences the social alignment decisions of their connections on the social network. We further find that such social alignment decisions are significantly impacted by population heterogeneity.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kbs21,

title = {Learning Product Representations for Generating Reviews for Cold Products},

author = {Fatemeh Pourgholamali and Mohsen Kahani and Zeinab Noorian and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s095070512100544x-main/},

doi = {https://doi.org/10.1016/j.knosys.2021.107282},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Knowledge-based Systems},

abstract = {Existing work in the literature have shown that the number and quality of product ratings andreviews have a direct correlation with the product purchase rates in online e-commerce portals.However, the majority of the products on e-commerce portals do not have any ratings or reviewsand are known as cold products (∼90% of products on Amazon are cold). As such, there has beengrowing interest in generating reviews for cold products by selectively transferring reviews fromother similar yet warm products. Our work in this paper focuses on this specific problem and gen-erates reviews for cold products through review selection. Similar to existing work in the literature,our work assumes a relationship between product attribute-values and the reviews that productsreceive. However, unlike the literature, our method (1) is not restricted to the exact surface formof a product attribute name; and, (2) can distinguish between the same attribute expressed in dif-ferent forms. We achieve these two important characteristics by proposing methods to learn neuralproduct representations that capture the semantics of product attribute-values as they relate touser reviews. More specifically, our work offers (i) an approach to learn neural representationsof product attribute-values within a shared embedding space as product reviews; (ii) a weightedcomposition strategy to develop product representations from the representation of its attributes;and, (iii) a review selection method that selects relevant reviews for the composed product repre-sentation within the neural embedding space. We show through our extensive experiments on fivedatasets consisting of products fromCNET.comand movies fromrottentomatoes.comthat ourmethod is able to show stronger performance compared to several baselines on ROUGE-2 metrics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2021a,

title = {Learning to Rank Implicit Entities on Twitter},

author = {Hawre Hosseini and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0306457321000145-main/},

doi = {https://doi.org/10.1016/j.ipm.2021.102503},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Information Processing and Management},

abstract = {Linking textual content to entities from the knowledge graph has received increasing attention in the context of which surface form representations of entities, e.g., terms or phrases, are disambiguated and linked to appropriate entities. This allows textual content, e.g., social user-generated content, to be interpreted and reasoned on at a higher semantic level. However, recent research has shown that at least 15% of social user-generated content do not have explicit surface form representation of entities that they discuss. In other words, the subject of the content is only implied. For such cases, existing entity linking methods, known as explicit entity linking, cannot perform linking because entity surface form is missing. In this paper, we investigate how implicit entities within social content can be identified and linked. The contributions of our work include (1) modeling the problem of implicit entity linking as a learn to rank problem where knowledge graph entities are ranked based on their relevance to the input tweet, (2) the introduction and systematic classification of appropriate features for identifying implicit entities, (3) extensive evaluation of the proposed approach in comparison with existing state of the art as well as performing feature analysis over proposed features, and (4) the qualitative assessment of the root causes for mislabeled instances in our experiments and careful discussion on how mislabeled entity links can be addressed as a part of future work. In our experiments, we show that our proposed features are able to improve the state of the art over the standard Precision at 1 (P@1) metric.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cscw21,

title = {On the Congruence Between Online Social Content and Future IT Skill Demand},

author = {Jalehsadat Mahdavimoghaddam and Niranjan Krishnaswamy and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/3479511/},

doi = {https://doi.org/10.1145/3479511},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Proc. ACM Hum. Comput. Interact. },

volume = {5(CSCW2)},

pages = {1-27},

abstract = {The speed of digital transformation has resulted in new challenges for job seekers to become lifelong learners and to develop new skills faster than before. In this paper, our main objective is to examine how online content can serve as indicators for changes to the Information Technology (IT) industry and its in-demand skills. To study this relationship, we collect Reddit posts to represent social media content and job postings to reflect the IT industry based on which we explore possible correlations between them. Further, we propose a methodology to quantitatively estimate the predictive power of social media content for future in-demand skills. Our results show that the frequency of skill-related conversations on Reddit correlates with the popularity of skills in job posting data. Additionally, our findings indicate that the number of social posts dedicated to a specific skill can be a strong indicator for future job requirements. This is an important finding because identifying what skills the labor force should acquire will assist job seekers to plan their lifelong learning objectives to (a) maximize their employability, (b) continuously update their skills to remain in demand, and (c) be informed and actively engaged in defining knowledge trends, rather than reactively becoming informed of the latest information.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{slr2021,

title = {Text Mining to Support Abstract Screening for Knowledge Syntheses: A Semi-Automated Workflow},

author = {Ba’ Pham and Jelena Jovanovic and Ebrahim Bagheri and Jesmin Antony and Huda Ashoor and Tam T Nguyen and Patricia Rios and Reid C Robson and Sonia M Thomas and Jennifer Watt and Sharon E Straus and Andrea C Tricco},

url = {https://ls3.rnet.torontomu.ca/s13643-021-01700-x/},

doi = {https://doi.org/10.1186/s13643-021-01700-x},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Systematic Reviews},

abstract = {Background: Current text mining tools supporting abstract screening in systematic reviews are not widely used, in part because they lack sensitivity and precision. We set out to develop an accessible, semi-automated “workflow” to conduct abstract screening for systematic reviews and other knowledge synthesis methods. Methods: We adopt widely recommended text-mining and machine-learning methods to 1) process title-abstracts into numerical training data; and 2) train a classification model to predict eligible abstracts. The predicted abstracts are screened by human reviewers for (“true”) eligibility, and the newly eligible abstracts used to identify (using near-neighbor methods) similar abstracts, which are also screened. These abstracts, as well as their eligibility results, are used to update the classification model, and the above steps are iterated until no new eligible abstracts are identified. The workflow was implemented in R and evaluated using a systematic review of insulin-formulations for type-1 diabetes (14,314 abstracts) and a scoping review of knowledge-synthesis methods (17,200 abstracts). Workflow performance was evaluated against the recommended practice of screening abstracts by 2 reviewers, independently. Standard measures were examined: sensitivity (inclusion of all truly eligible abstracts), specificity (exclusion of all truly ineligible abstracts), precision (inclusion of all truly eligible abstracts among all abstracts screened as eligible), F1-score (harmonic average of sensitivity and precision), and accuracy (correctly predicted eligible or ineligible abstracts). Workload reduction was measured as the hours the workflow saved, given only a subset of abstracts needed human screening. Results: With respect to the systematic and scoping reviews, the workflow attained 88%/89% sensitivity, 99%/99% specificity, 71%/72% precision, an F1-score of 79%/79%, 98%/97% accuracy, 63%/55% workload reduction, with 12%/11% fewer abstracts for full-text retrieval and screening, and 0/6 1.5% missed studies in the completed reviews. Conclusion: The workflow was a sensitive, precise, and efficient alternative to the recommended practice of screening abstracts with 2 reviewers. All eligible studies were identified in the first case, while 6 studies (1.5%) were missed in the second that would likely not impact the review’s conclusions. We have described the workflow in language accessible to reviewers with limited exposure to natural language processing and machine learning, and have made the code accessible to reviewers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2020b,

title = {Extracting Temporal and Causal Relations based on Event Networks},

author = {Duc-Thuan Vo and Feras Al-Obeidat and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0306457320308141-main_compressed/},

doi = {https://doi.org/10.1016/j.ipm.2020.102319},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Information Processing and Management},

abstract = {Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical structure patterns at the sentence-level. In this paper, we address these challenges by proposing an unsupervised event network representation for temporal and causal relation extraction that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal and causal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 and Causal-TimeBank corpora and compare our work with several strong baselines. We show that our method improves performance compared to several strong methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fntir20,

title = {Extracting, Mining and Predicting Users' Interests from Social Media},

author = {Fattane Zarrinkalam and Stefano Faralli and Guangyuan Piao and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/9781680837391-summary/},

doi = {http://dx.doi.org/10.1561/1500000078},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Foundations and Trends in Information Retrieval (FnTIR)},

abstract = {The abundance of user generated content on social media provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users' interests and preferences. In this paper, we will cover five important subjects related to the mining of user interests from social media: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2020a,

title = {Neural Embedding-based Specificity Metrics for Pre-Retrieval Query Performance Prediction},

author = {Negar Arabzadeh and Fattane Zarrinkalam and Jelena Jovanovic and Feras Al-Obeidat and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s030645731931266x-main-3_compressed/},

doi = {https://doi.org/10.1016/j.ipm.2020.102248},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Information Processing and Management},

abstract = {In information retrieval, the task of query performance prediction (QPP) is concerned with determining in advance the performance of a given query within the context of a retrieval model. QPP has an important role in ensuring proper handling of queries with varying levels of difficulty. Based on the extant literature, textitquery specificity is an important indicator of query performance and is typically estimated using corpus-specific frequency-based specificity metrics However, such metrics do not consider term semantics and inter-term associations. Our work presented in this paper distinguishes itself by proposing a host of corpus-independent specificity metrics that are based on pre-trained neural embeddings and leverage geometric relations between terms in the embedding space in order to capture the semantics of terms and their interdependencies. Specifically, we propose three classes of specificity metrics based on pre-trained neural embeddings: neighborhood-based, graph-based, and cluster-based metrics. Through two extensive and complementary sets of experiments, we show that the proposed specificity metrics (1) are suitable specificity indicators, based on the gold standards derived from knowledge hierarchies (Wikipedia category hierarchy and DMOZ taxonomy), and (2) have better or competitive performance compared to the state of the art QPP metrics, based on both TREC ad hoc collections namely Robust'04, Gov2 and ClueWeb'09 and ANTIQUE question answering collection. The proposed graph-based specificity metrics, especially those that capture a larger number of inter-term associations, proved to be the most effective in both query specificity estimation and QPP. We have also publicly released two test collections (i.e. specificity gold standards) that we built from the Wikipedia and DMOZ knowledge hierarchies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jasist2020,

title = {On The Causal Relation Between Real World Activities and Emotional Expressions of Social Media Users},

author = {Seyed Amin Mirlohi Falavarjani and Jelena Jovanovic and Hossein Fani and Ali A Ghorbani and Zeinab Noorian and Ebrahim Bagheri},

url = {https://asistdl.onlinelibrary.wiley.com/journal/23301643},

doi = {https://doi.org/10.1002/asi.24440},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Journal of the Association for Information Science and Technology (JASIST)},

abstract = {Social interactions through online social media have become a daily routine of many, and the number of those whose real-world (offline) and online lives have become intertwined is continuously growing. As such, the interplay of individuals' online and offline activities has been the subject of numerous research studies, the majority of which explored the impact of people's online actions on their offline activities. The opposite direction of impact - the effect of real-world activities on online actions - has also received attention but to a lesser degree. To contribute to the latter form of impact, this paper reports on a quasi-experimental design study that examined the presence of causal relations between real-world activities of online social media users and their online emotional expressions. To this end, we have collected a large dataset (over 17K users) from Twitter and Foursquare, and systematically aligned user content on the two social media platforms. Users' Foursquare check-ins provided information about their offline activities, whereas the users' expressions of emotions and moods were derived from their Twitter posts. Since our study was based on a quasi-experimental design, to minimise the impact of covariates, we applied an innovative model of computing propensity scores. Our main findings can be summarised as follows: (i) users' offline activities do impact their affective expressions, both of emotions and moods, as evidenced in their online shared textual content; (ii) the impact depends on the type of offline activity and if the user embarks on or abandons the activity. Our findings can be used to devise a personalised recommendation mechanism to help people better manage their online emotional expressions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2020f,

title = {Semantics-enabled Query Performance Prediction for Ad hoc Table Retrieval},

author = {Maryam Khodabakhsh and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0306457320308943-main/},

doi = {https://doi.org/10.1016/j.ipm.2020.102399},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Information Processing and Management},

abstract = {Predicting the performance of a retrieval method for a given query is a highly important and challenging problem in information retrieval. Accurate Query Performance Prediction (QPP) plays an important role in real time handling of queries with varying levels of difficulty. While there have been several successful query performance predictors, no predictors have yet been introduced within the context of the ad hoc table retrieval task, which is concerned with answering a query with a ranked list of tables. In this paper, we propose to perform query performance prediction based on neural embedding techniques for ad hoc table retrieval and introduce three neural features. The neural features are based on neural embedding techniques and leverage the distance between tokens in the embedding space in order to capture their semantic similarity. We evaluate our proposed work based on a gold standard test collection and compare it with the state-of-the-art post-retrieval query performance prediction methods. We find that our neural features (1) are effective for predicting the performance of content-based ranking functions; and not as effective for feature-based ranking functions, and (2) show a synergistic impact on existing QPP methods and hence are able to increase their performance in practice.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ECRA2019,

title = {A Neural Graph Embedding Approach for Selecting Review Sentences},

author = {Fatemeh Pourgholamali and Mohsen Kahani and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s1567422319300948-main_compressed-1/},

doi = {https://doi.org/10.1016/j.elerap.2019.100917},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Electronic Commerce Research and Applications},

abstract = {Product reviews written by the crowd on e-commerce shopping websites have become a critical information source for making purchasing decisions. Researchers have already extensively studied the impact of review sentiments and informativeness on product sales and success, finding a close relationship. An important challenge, however, is that the vast majority of products (e.g., >90% of products on amazon.com) do not receive enough attention and lack sufficient reviews by the users; hence, they constitute the so-called textitcold products. One solution to address cold products, which has already been studied in the literature, is to generate reviews for these products by sampling review sentences from closely related warm products. Our method proposed in this paper is specifically focused on such a solution. While a majority of the works in the literature rely on product specification similarity to identify relevant reviews that can be used for review sentence selection, our work differs in that it not only employs product specification similarity but also employs product-review, product-user, and user-review interactions when determining the suitability of a review sentence to be selected. More specifically, the contributions of our work can be enumerated as follows: (1) We propose that the selection of review sentences from other products should not only consider product-product similarity but also consider product-review, user-review, and user-user relationships. As such, we show how neural graph embeddings can be used to encode product, user, and review information into an attributed heterogeneous graph representation based on which similarities can be calculated. (2) We further propose how review textitrelevance and textitimportance can be considered using graph traversal to select appropriate review sentences for a given cold product. (3) Finally, we systematically compare the performance of our work with those of several state-of-the-art baselines on five datasets collected from CNET.com and rottentomatoes.com with different characteristics from both quantitative (e.g., the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics) and qualitative aspects and show how our proposed approach was able to provide statistically significantly improved performance over various strong baselines.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ao2019,

title = {Implicit Entity Linking in Tweets: an Ad-hoc Retrieval Approach},

author = {Hawre Hosseini and Tam T Nguyen and Jimmy Wu and Ebrahim Bagheri},

url = {https://www.iospress.nl/journal/applied-ontology/},

doi = {https://doi.org/10.3233/AO-190215},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Applied Ontology},

abstract = {Within the context of Twitter analytics, the notion of implicit entity linking has recently been introduced to refer to the identification of a named entity, which is central to the topic of the tweet, but whose surface form is not present in the tweet itself. Compared to traditional forms of entity linking where the linking process revolves around an identified surface form of a potential entity, implicit entity linking relies on contextual clues to determine whether an implicit entity is present within a given tweet and if so, which entity is being referenced. The objective of this paper, while introducing and publicly sharing a comprehensive gold standard dataset for implicit entity linking, is to perform the task of implicit entity linking. The dataset consists of 7,870 tweets, which are classified as either containing implicit entities, explicit entities, both, or neither. The implicit entities are then linked to three levels of entities on Wikipedia, namely coarse-grained level, e.g., Person, Fine-grained level, e.g., Comedian, and the actual entity, e.g., Seinfeld. The proposed model in this work formulates the problem of implicit entity linking as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of textual descriptions of entities in the knowledge base. The novel contributions of our work include: 1) designing and collecting a gold standard dataset for the task of implicit entity linking; 2) defining the implicit entity linking process as an ad-hoc document retrieval task; and 3) proposing a neural embedding-based feature function that is interpolated with prior term dependency and entity-based feature functions to enhance implicit entity linking. We systematically compare our work with a state of the art baseline and show that our method is able to provide statistically significant improvements.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fgcs2019,

title = {The Evolutionary Composition of Desirable Execution Traces from Event Logs},

author = {Asef Pourmasoumi and Mohsen Kahani and Ebrahim Bagheri},

url = {http://ls3.rnet.ryerson.ca/wp-content/uploads/2019/03/fgcs.pdf},

doi = {https://doi.org/10.1016/j.future.2019.03.037},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Future Generation Computing Systems},

volume = {98},

pages = {78-103},

abstract = {In this paper, we propose an evolutionary computing approach based on Genetic Algorithms for composing an efficient trace given a desirable utility function based on the observations made in the event logs of several peer-organizations. Our proposed approach works with a set of event logs from different peer-organizations and generates an efficient trace according to a utility function. The main advantage of our approach is that we primarily work with event logs that are more accurate representations of the actual execution of a process within an organization. Furthermore, we generate efficient traces that are put together through the identification of sub-parts of the observed traces that are locally optimal. We report on our experiments on the BPIC'15 dataset that show improvement in terms of the optimality of the generated traces.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2019e,

title = {The Reflection of Offline Activities on Users’ Online Social Behavior: An Observational Study},

author = {Seyed Amin Mirlohi Falavarjani and Fattane Zarrinkalam and Jelena Jovanovic and Ebrahim Bagheri and Ali A Ghorbani},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0306457318309981-main_compressed/},

doi = {https://doi.org/10.1016/j.ipm.2019.102070},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Information Processing and Management},

abstract = {The ever increasing presence of online social networks in users’ daily lives has led to the interplay between users’ online and offline activities. There have already been several works that have studied the impact of users’ online activities on their offline behavior, e.g., the impact of interaction with friends on an exercise social network on the number of daily steps. In this paper, we consider the inverse to what has already been studied and report on our extensive study that explores the potential causal effects of users’ offline activities on their online social behavior. The objective of our work is to understand whether the activities that users are involved with in their real daily life, which place them within or away from social situations, have any direct causal impact on their behavior in online social networks. Our work is motivated by the theory of normative social influence, which argues that individuals may show behaviors or express opinions that conform to those of the community for the sake of being accepted or from fear of rejection or isolation. We have collected data from two online social networks, namely Twitter and Foursquare, and systematically aligned user content on both social networks. On this basis, we have performed a natural experiment that took the form of an interrupted time series with a comparison group design to study whether users’ socially situated offline activities exhibited through their Foursquare check-ins impact their online behavior captured through the content they share on Twitter. Our main findings can be summarised as follows (1) a change in users’ offline behaviour that affects the level of users’ exposure to social situations, e.g., starting to go to the gym or discontinuing frequenting at bars, can have a causal impact on users’ online topical interests and sentiment; and (2) the causal relations between users’ socially situated offline activities and their online social behavior can be used to build effective predictive models of users’ online topical interests and sentiments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2019d,

title = {User Community Detection via Embedding of Social Network Structure and Temporal Content},

author = {Hossein Fani and Eric Jiang and Ebrahim Bagheri and Feras Al-Obeidat and Weichang Du and Mehdi Kargar},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s030645731830997x-main_compressed/},

doi = {https://doi.org/10.1016/j.ipm.2019.102056},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Information Processing and Management},

abstract = {Identifying and extracting user communities is an important step towards understanding social network dynamics from a macro perspective. For this reason, the work in this paper explores various aspects related to the identification of user communities. To date, user community detection methods employ either explicit links between users (link analysis), or users' topics of interest in posted content (content analysis), or in tandem. Little work has considered temporal evolution when identifying user communities in a way to group together those users who share not only similar topical interests but also similar temporal behavior towards their topics of interest. In this paper, we identify user communities through textitmultimodal feature learning (embeddings). Our core contributions can be enumerated as (a) we propose a new method for learning neural embeddings for users based on their temporal content similarity; (b) we learn user embeddings based on their social network connections (links) through neural graph embeddings; (c) we systematically interpolate temporal content-based embeddings and social link-based embeddings to capture both social network connections and temporal content evolution for representing users, and (d) we systematically evaluate the quality of each embedding type in isolation and also when interpolated together and demonstrate their performance on a Twitter dataset under two different application scenarios, namely textitnews recommendation and textituser prediction. We find that (1) content-based methods produce higher quality communities compared to link-based methods; (2) methods that consider temporal evolution of content, our proposed method in particular, show better performance compared to their non-temporal counter-parts; (3) communities that are produced when time is explicitly incorporated in user vector representations have higher quality than the ones produced when time is incorporated into a generative process, and finally (4) while link-based methods are weaker than content-based methods, their interpolation with content-based methods leads to improved quality of the identified communities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jss2018-2,

title = {Automated Composition and Optimization of Services for Variability-Intensive Domains},

author = {Mahdi Bashari and Ebrahim Bagheri and Weichang Du},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0164121218301481-main_compressed/},

doi = {https://doi.org/10.1016/j.jss.2018.07.039},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {​Journal of Systems and Software},

abstract = {The growth in the number of publicly available services on the Web has encouraged developers to rely more heavily on such services to deliver products in a faster, cheaper and more reliable fashion. Many developers are now using a collection of these services in tandem to build their applications. While there has been much attention to the area of service composition, there are few works that examine the possibility of automatically generating service compositions for variability-intensive application domains. High variability in a domain is often captured through an organized feature space, which has the potential for developing many different application instantiations. The focus of our work is to develop an end-to-end technique that would enable the automatic generation of composite services based on a specific configuration of the feature space that would be directly executable and presented in WS-BPEL format. To this end, we adopt concepts from software product line engineering and AI planning to deliver the automated composition of online services. We will further benefit from such notions as safeness and threat from AI planning to optimize the generated service compositions by introducing parallelism where possible. Furthermore, we show how the specification of the generated service composition can be translated into executable WS-BPEL code. More specifically, the core contributions of our work are: (1) we show how AI planning techniques can be used to generate a workflow based on a feature model configuration; (2) we propose a method for optimizing a workflow generated based on AI planning techniques; and (3) we demonstrate that the optimized workflow can be directly translated into WS-BPEL code. We evaluate our work from two perspectives: i) we will first formally prove that the methods that we have proposed are sound and complete from a theoretical perspective, and ii) we will show through experimentation that our proposed work is usable from a practical point of view.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipl2018,

title = {Building a business domain meta-ontology for information pre-processing},

author = {Yevgen Biletskiy and Anthony J Brown and Girish R Ranganathan and Ebrahim Bagheri and Ismail Akbari},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0020019018301340-main/},

doi = {https://doi.org/10.1016/j.ipl.2018.06.009},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Information Processing Letters},

abstract = {Business analysts, along with other business domain software application users, have created a vast amount of business documents, which often do not have any business domain ontologies in the background. This situation leads to misinterpretation of such documents, when being processed by machines, that results in inhibiting the productiveness of computer-assisted analytical work and effectiveness of business solutions due to lack of effective semantics; therefore, business analysts (especially, if rotating) can use well-designed business domain ontologies as a backbone for their official applications. The process of extracting and capturing domain ontologies from these voluminous documents requires extensive involvement of domain experts and application of methods of ontology learning that is substantially labor intensive; therefore, some intermediate solutions which would assist in capturing business domain ontologies must be developed. The present paper proposes a solution in this direction which involves building a meta-ontology as a rapid approach in conceptualizing a business domain from huge amounts of source documents. This meta-ontology can be populated by ontological concepts, attributes and relations from business documents, and then refined in order to form better business domain ontology either through automatic ontology learning methods or some other traditional ontology building approaches.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{knosys18,

title = {Detecting Life Events From Twitter based on Temporal Semantic Features},

author = {Maryam Khodabakhsh and Mohsen Kahani and Ebrahim Bagheri and Zeinab Noorian},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s095070511830073x-main_compressed/},

doi = {https://doi.org/10.1016/j.knosys.2018.02.021},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Knowledge-based Systems},

abstract = {The wide adoption of social networking and microblogging platforms by a large number of users across the globe has provided a rich source of unstructured information for understanding users' behaviors, interests and opinions at both micro and macro levels. An active area in this space is the detection of important real-world events from user-generated social content. The works in this area identify instances of events that impact a large number of users. However, a more nuanced form of an event, known as life event, is also of high importance, which in contrast to real-world events, does not impact a large number of users and is limited to at most a few people. For this reason, life events, such as marriage, travel, and career change, among others, are more difficult to detect for several reasons: i) they are specific to a given user and do not have a wider reaching reflection; ii) they are often not reported directly and need to be inferred from the content posted by individual users; and iii) many users do not report their life events on social platforms, making the problem highly class-imbalanced. In this paper, we propose a semantic approach based on word embedding techniques to model life events. We then use word mover's distance to measure the similarity of a given tweet to different types of life events, which are used as input features for a multi-class classifier. Furthermore, we show that when a sequence of tweets that have appeared before and after a given tweet of interest (temporal stacking) are considered, the performance of the life event detection task improves significantly.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{snam2018,

title = {Entity Linking of Tweets based on Dominant Entity Candidates},

author = {Yue Feng and Fattane Zarrinkalam and Ebrahim Bagheri and Hossein Fani and Feras Al-Obeidat},

url = {https://ls3.rnet.torontomu.ca/s13278-018-0523-0/},

doi = {https://doi.org/10.1007/s13278-018-0523-0},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Social Network Analysis and Mining},

abstract = {Entity linking, also known as semantic annotation, of textual content has received increasing attention. Recent works in this area have focused on entity linking on text with special characteristics such as search queries and tweets. The semantic annotation of tweets is specially proven to be challenging given the informal nature of the writing and the short length of the text. In this paper, we propose a method to perform entity linking on tweets built based on one primary hypothesis. We hypothesize that while there are formally many possible entity candidates for an ambiguous mention in a tweet, as listed on the disambiguation page of the corresponding entity on Wikipedia, there are only few entity candidates that are likely to be employed in the context of Twitter. Based on this hypothesis, we propose a method to identify such dominant entity candidates for each ambiguous mention and use them in the annotation process. Particularly, our proposed work integrates two phases i) dominant entity candidate detection, which applies community detection methods for finding the dominant candidates of ambiguous mentions; and ii) named entity disambiguation that links a tweet to entities in Wikipedia by only considering the identified dominant entity candidates. Our investigations show that: 1) there are only very few entity candidates for each ambiguous mention in a tweet that need to be considered when performing disambiguation. This helps us limit the candidate search space and hence noticeably reduce the entity linking time; 2) limiting the search space to only a subset of disambiguation options will not only improve entity linking execution time but will also lead to improved accuracy of the entity linking process when the main entity candidates of each mention are mined from a temporally aligned corpus. We show that our proposed method offers competitive results with the state-of-the-art methods in terms of precision and recall on widely-used gold standard datasets while significantly reducing the time for processing each tweet. year = 2018},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2018-thuan,

title = {Feature-enriched Matrix Factorization for Relation Extraction},

author = {Duc-Thuan Vo and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0306457318303157-main_compressed/},

doi = {https://doi.org/10.1016/j.ipm.2018.10.011},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Information Processing and Management},

abstract = {Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/ci/FaniBZZD17,

title = {Finding Diachronic Like-Minded Users},

author = {Hossein Fani and Ebrahim Bagheri and Fattane Zarrinkalam and Xin Zhao and Weichang Du},

url = {http://onlinelibrary.wiley.com/doi/10.1111/coin.12117/full},

doi = {10.1111/coin.12117},

issn = {1467-8640},

year  = {2018},

date = {2018-01-01},

journal = {Computational Intelligence: An International Journal},

volume = {34},

pages = {124-144},

publisher = {Wiley Periodicals, Inc.},

abstract = {User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, just to name a few, they do not necessarily succeed at pulling likeminded users that share the same interests together. Therefore, researchers have explored the topical similarity of social content to build like-minded communities of users. In this paper, following the topic-based approaches, we are interested in identifying communities of users that share similar topical interests with similar temporal behavior. More specifically, we tackle the problem of identifying temporal (diachronic) topic-based communities, i.e., communities of users who have a similar temporal inclination towards emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user towards emerging topics. Further, our modeling is completely agnostic to the topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph-based and two LDA-based methods. Through our experiments on Twitter data, we demonstrate the effectiveness of our proposed temporal topic-based community detection method in the context of news recommendation, user prediction, and document timestamp prediction applications, compared to the non-temporal as well as the state-of-the-art temporal approaches.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jce2018,

title = {Improving the Conduct of Systematic Reviews: A Process Mining Perspective},

author = {Ba' Pham and Ebrahim Bagheri and Patricia Rios and Asef Pourmasoumi and Reid C Robson and Jeremiah Hwee and Wanrudee Isaranuwatchai and Matthew Page Nazia Darvesh and Andrea Tricco},

url = {https://www.journals.elsevier.com/journal-of-clinical-epidemiology},

year  = {2018},

date = {2018-01-01},

journal = {Journal of Clinical Epidemiology},

abstract = {Objectives: To demonstrate the feasibility of using process mining concepts, techniques, and tools to examine and improve the systematic review process. Study Design and Setting: We conducted a simulation study evaluating a process used by one research team over one year. The process was characterized using an event log of review activities, start/end dates for review tasks, reviewers, and person-hours spent on tasks. We obtained process models from mining event logs for visual display/animation/replay of review activities. We analyzed the social networks of reviewer interactions to discern how reviewers worked together. Key outcomes included review timelines and person-time. Results: The 12 reviews included in the study included an average of 3831 titles and abstracts (range:1565-6368) and 20 studies (6-42). The average time was 463 days (range: 289-629) [881 person-hours (range: 243-1752)] per review. The average person-hours on each step were: study selection 26%, data abstraction 24%, report preparation 23%, and meta-analysis 17%. Social network analyses showed that the team handled tasks according to their expected roles (e.g., methodologists developed review questions, librarians conducted searches, and review coordinators coordinated tasks). Conclusion: Process mining is valuable for review teams interested in improving and modernizing the conduct of systematic reviews.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IMP2017b,

title = {Mining User Interests over Active Topics on Social Networks},

author = {Fattane Zarrinkalam and Mohsen Kahani and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0306457317307070-main/},

doi = {https://doi.org/10.1016/j.ipm.2017.12.003},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Information Processing and Management},

abstract = {Inferring users' interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users' implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users' implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users' homophily relation impact the quality of inferring users' implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users' interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2018-fatemehl,

title = {Neural Embedding-based Indices for Semantic Search},

author = {Fatemeh Lashkari and Ebrahim Bagheri and Ali A Ghorbani},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0306457318302413-main_compressed/},

doi = {https://doi.org/10.1016/j.ipm.2018.10.015},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Information Processing and Management},

pages = {to appear},

abstract = {Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the emphvocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both empheffectiveness and emphefficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) textitretrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, textitretrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ipm2018-2,

title = {Neural Word and Entity Embeddings for Ad hoc Retrieval},

author = {Ebrahim Bagheri and Faezeh Ensan and Feras Al-Obeidat},

url = {http://ls3.rnet.ryerson.ca/wiki/images/2/29/NeuralEmbeddings_IPM.pdf},

doi = {https://doi.org/10.1016/j.ipm.2018.04.007},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Information Processing and Management year = 2018},

volume = {54},

pages = {339–357},

abstract = {Learning low dimensional dense representations of the vocabularies of a corpus, known as neural embeddings, has gained much attention in the information retrieval community. While there have been several successful attempts at integrating embeddings within the ad hoc document retrieval task, yet, no systematic study has been reported that explores the various aspects of neural embeddings and how they impact retrieval performance. In this paper, we perform a methodical study on how neural embeddings influence the ad hoc document retrieval task. More specifically, we systematically explore the following research questions: i) do methods solely based on neural embeddings perform competitively with state of the art retrieval methods with and without interpolation? ii) are there any statistically significant difference between the performance of retrieval models when based on textitword embeddings compared to when knowledge graph entity embeddings are used? and iii) is there significant difference between using locally trained neural embeddings compared to when globally trained neural embeddings are used? We examine these three research questions across both hard and all queries.Our study finds that word embeddings do not show competitive performance to any of the baselines. In contrast, entity embeddings show competitive performance to the baselines and when interpolated, outperform the best baselines for both hard and soft queries.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{psychosom18,

title = {Online social network response to studies on antidepressant use in pregnancy},

author = {Simone N Vigod and Ebrahim Bagheri and Fattane Zarrinkalam and Hillary K Brown and Muhammad Mamdani and Joel G Ray},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0022399917312059-main_compressed/},

doi = {https://doi.org/10.1016/j.jpsychores.2018.01.009},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Journal of Psychosomatic Research},

abstract = {Background: About 8% of U.S women are prescribed antidepressant medications around the time of pregnancy. Decisions about medication use in pregnancy can be swayed by the opinion of family, friends and online media, sometimes beyond the advice offered by healthcare providers. Exploration of the online social network response to research on antidepressant use in pregnancy could provide insight about how to optimize decision-making in this complex area. Methods: For all 17 research articles published on the safety of antidepressant use in pregnancy in 2012, we sought to explore online social network activity regarding antidepressant use in pregnancy, via Twitter, in the 48 hours after a study was published, compared to the social network activity in the same period 1 week prior to each article’s publication. Results: Online social network activity about antidepressants in pregnancy quickly doubled upon study publication. The increased activity was driven by studies demonstrating harm associated with antidepressants, by lower-quality studies, and studies where abstracts presented relative versus absolute risks. Implications: These findings support a call for leadership from medical journals to consider how to best incentivize and support a balanced and clear translation of knowledge around antidepressant safety in pregnancy to their readership and the public.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jiis2018b,

title = {Predicting Future Personal Life Events on Twitter via Recurrent Neural Networks},

author = {Maryam Khodabakhsh and Mohsen Kahani and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/s10844-018-0519-2/},

doi = {https://doi.org/10.1007/s10844-018-0519-2},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Journal of Intelligent Information Systems},

abstract = {Social network users publicly share a wide variety of information with their followers and the general public ranging from their opinions, sentiments and personal life activities. There has already been significant advance in analyzing the shared information from both micro (individual user) and macro (community level) perspectives, giving access to actionable insight about user and community behaviors. The identification of personal life events from user's profiles is a challenging yet important task, which if done appropriately, would facilitate more accurate identification of users' preferences, interests and attitudes. For instance, a user who has just broken his phone, is likely to be upset and also be looking to purchase a new phone. While there is work that identifies tweets that include mentions of personal life events, our work in this paper goes beyond the state of the art by predicting a future personal life event that a user will be posting about on Twitter solely based on the past tweets. We propose two architectures based on recurrent neural networks, namely the classification and generation architectures, that determine the future personal life event of a user. We evaluate our work based on a gold standard Twitter life event dataset and compare our work with the state of the art baseline technique for life event detection. While presenting performance measures, we also discuss the limitations of our work in this paper.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jss2018-1,

title = {Self-Adaptation of Service Compositions through Product Line Reconfiguration},

author = {Mahdi Bashari and Ebrahim Bagheri and Weichang Du},

url = {https://ls3.rnet.torontomu.ca/1-s2-0-s0164121218301134-main_compressed/},

doi = {https://doi.org/10.1016/j.jss.2018.05.069},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {​Journal of Systems and Software},

volume = {114},

pages = {84--105},

abstract = {The large number of published services has motivated the development of tools for creating customized composite services known as service compositions. While service compositions provide high agility and development flexibility, they can also pose challenges when it comes to delivering guaranteed functional and non-functional requirements. This is primarily due to the highly dynamic environment in which services operate. In this paper, we focus on maintaining functional and non-functional requirements of service compositions within high variability domains, which are often modeled through software product line techniques. One of the main characteristics of high variability application domains is that they are rich in functional features whose configuration results in an instantiated functional application. We propose adaptation mechanisms that are able to effectively maintain functional and non-functional quality requirements in service compositions derived from software product lines. Unlike many existing work, the proposed adaptation mechanism does not require explicit user-defined adaptation strategies. We adopt concepts from the software product line engineering paradigm where service compositions are viewed as a collection of features and adaptation happens through product line reconfiguration. We have practically implemented the proposed mechanism in ourtextit Magus tool suite and performed extensive experiments, which show that our work is both practical and efficient for automatically adapting service compositions once violations of functional or non-functional requirements are observed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jiis2018c,

title = {Topic and Sentiment Aware Microblog Summarization for Twitter},

author = {Syed Muhammad Ali and Zeinab Noorian and Ebrahim Bagheri and Chen Ding and Feras Al-Obeidat},

url = {https://ls3.rnet.torontomu.ca/s10844-018-0521-8/},

doi = {https://doi.org/10.1007/s10844-018-0521-8},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Journal of Intelligent Information Systems},

abstract = {Recent advances in microblog content summarization has primarily viewed this task in the context of traditional multi-document summarization techniques where a microblog post or their collection form one document. While these techniques already facilitate information aggregation, categorization and visualization of microblog posts, they fall short in two aspects: i) when summarizing a certain topic from microblog content, not all existing techniques take topic polarity into account. This is an important consideration in that the summarization of a topic should cover all aspects of the topic and hence taking polarity into account (sentiment) can lead to the inclusion of the less popular polarity in the summarization process. ii) Some summarization techniques produce summaries at the topic level. However, it is possible that a given topic can have more than one important aspect that need to have representation in the summarization process. Our work in this paper addresses these two challenges by considering both topic sentiments and topic aspects in tandem. We compare our work with the state of the art Twitter summarization techniques and show that our method is able to outperform existing methods on standard metrics such as ROUGE-1.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jamia18,

title = {UMLS to DBPedia Link Discovery Through Circular Resolution},

author = {John Cuzzola and Ebrahim Bagheri and Jelena Jovanovic},

url = {https://ls3.rnet.torontomu.ca/ocy021/},

doi = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647029/#:~:text=%C2%A010.1093/jamia/ocy021},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Journal of the American Medical Informatics Association (JAMIA)},

abstract = {Objective: The goal of this work is to map UMLS concepts to DBpedia resources using widely accepted ontology relations including skos:exactMatch, skos:closeMatch, and rdfs:seeAlso, as a result of which a complete mapping from UMLS to DBpedia is made publicly available that includes 221,690 skos:exactMatch, 26,276 skos:closeMatch, and 6,784,322 rdfs:seeAlso mappings. Materials and Methods: We propose a method called circular resolution that utilizes a combination of semantic annotators to map UMLS concepts to DBpedia resources. A set of annotators annotate definitions of UMLS concepts returning DBpedia resources while another set performs annotation on DBpedia resource abstracts returning UMLS concepts. Our pipeline aligns these two sets of annotations to determine appropriate mappings from UMLS to DBpedia. Results: We evaluate our proposed method using structured data from the Wikidata knowledge base as the ground truth, which consists of 4,899 already existing UMLS to DBpedia mappings. Our results show an 83% recall with 77% precision-at-one (P@1) in mapping UMLS concepts to DBpedia resources on this testing set. Conclusion: The proposed circular resolution method is a simple yet effective technique for linking UMLS concepts to DBpedia resources. Experiments using Wikidata-based ground truth reveal a high mapping accuracy. In addition to the complete UMLS mapping downloadable in n-triple format, we provide an online browser and a RESTful service to explore the mappings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{inrt2018,

title = {User Interest Prediction over Future Unobserved Topics on Social Networks},

author = {Fattane Zarrinkalam and Mohsen and Kahani and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/s10791-018-9337-y/},

doi = {https://doi.org/10.1007/s10791-018-9337-y},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Information Retrieval Journal},

abstract = {The accurate prediction of users' future interests on social networks allows one to perform future planning by studying how users will react if certain topics emerge in the future. It can improve areas such as targeted advertising and the efficient delivery of services. Despite the importance of predicting user future interests on social networks, existing works mainly focus on identifying user current interests and little work has been done on the prediction of user potential interests in the future. There have been work that attempt to identify a user future interests, however they cannot predict user interests with regard to new topics since these topics have never received any feedback from users in the past. In this paper, we propose a framework that works on the basis of temporal evolution of user interests and utilizes semantic information from knowledge bases such as Wikipedia to predict user future interests and overcome the cold item problem. Through extensive experiments on a real-world Twitter dataset, we demonstrate the effectiveness of our approach in predicting future interests of users compared to state-of-the-art baselines. Moreover, we further show that the impact of our work is especially meaningful when considered in case of cold items.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ijseke2016,

title = {Dynamic software product line engineering: a reference framework},

author = {Mahdi Bashari and Ebrahim Bagheri and Weichang Du},

url = {https://ls3.rnet.torontomu.ca/bashari-et-al-2017-dynamic-software-product-line-engineering-a-reference-framework/},

doi = {10.1142/S0218194017500085},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {International Journal of Software Engineering and Knowledge Engineering},

volume = {27},

pages = {191–234},

publisher = {World Scientific Publishing Company},

abstract = {Runtime adaptive systems are able to dynamically transform their internal structure,and hence their behavior, in response to internal or external changes. Such transformations provide the basis for new functionalities or improvements of the non-functional properties that match operational requirements and standards. Software Product Line Engineering (SPLE) has introduced several models and mechanisms for variability modeling and management. Dynamic software product lines (DSPL) engineering exploits the knowledge acquired in SPLE to develop systems that can be context-aware, post-deployment reconfigurable, or runtime adaptive. This paper focuses on DSPL engineering approaches for developing runtime adaptive systems and proposes a framework for classifying and comparing these approaches from two distinct perspectives: adaptation properties and adaptation realization. These two perspectives are linked together by a series of guidelines that help select a suitable adaptation realization approach based on desired adaptation types.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ECRA2017,

title = {Embedding Unstructured Side Information in Product Recommendation},

author = {Fatemeh Pourgholamali and Mohsen Kahani and Ebrahim Bagheri and Zeinab Noorian},

url = {https://ls3.rnet.torontomu.ca/j-elerap-2017-08-001/},

doi = {https://doi.org/10.1016/j.elerap.2017.08.001},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Electronic Commerce Research and Applications},

volume = {25},

pages = {70-851},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jiis2017,

title = {Query Expansion Using Pseudo Relevance Feedback on Wikipedia},

author = {Andisheh Keikha and Faezeh Ensan and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/s10844-017-0466-3-1/},

doi = {https://doi.org/10.1007/s10844-017-0466-3},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Journal of Intelligent Information Systems},

abstract = {One of the major challenges in Web search pertains to the correct interpretation of users’ intent. Query Expansion is one of the well-known approaches for determining the intent of the user by addressing the vocabulary mismatch problem. A limitation of the current query expansion approaches is that the relations between the query terms and the expanded terms is limited. In this paper, we capture users’ intent through query expansion. We build on earlier work in the area by adopting a pseudo-relevance feedback approach; however, we advance the state of the art by proposing an approach for feature learning within the process of query expansion. In our work, we specifically consider the Wikipedia corpus as the feedback collection space and identify the best features within this context for term selection in two supervised and unsupervised models. We compare our work with state of the art query expansion techniques, the results of which show promising robustness and improved precision.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{JBI2017,

title = {RysannMD: A Biomedical Semantic Annotator Balancing Speed and Accuracy},

author = {John Cuzzola and Jelena Jovanovic and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/j-jbi-2017-05-016/},

doi = {https://doi.org/10.1016/j.jbi.2017.05.016},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Journal of Biomedical Informatics},

abstract = {Recently, both researchers and practitioners have explored the possibility of semantically annotating large and continuously evolving collections of biomedical texts such as research papers, medical reports, and physician notes in order to enable their efficient and effective management and use in clinical practice or research laboratories. Such annotations can be automatically generated by biomedical semantic annotators - tools that are specifically designed for detecting and disambiguating biomedical concepts mentioned in text. The biomedical community has already presented several solid automated semantic annotators. However, the existing tools are either strong in their disambiguation capacity, i.e., the ability to identify the correct biomedical concept for a given piece of text among several candidate concepts, or they excel in their processing time, i.e., work very efficiently, but none of the semantic annotation tools reported in the literature has both of these qualities. In this paper, we present RysannMD (Ryerson Semantic Annotator for Medical Domain), a biomedical semantic annotation tool that strikes a balance between processing time and performance while disambiguating biomedical terms. In other words, RysannMD provides reasonable disambiguation performance when choosing the right sense for a biomedical term in a given context, and does that in a reasonable time. To examine how RysannMD stands with respect to the state of the art biomedical semantic annotators, we have conducted a series of experiments using standard benchmarking corpora, including both gold and silver standards, and four modern biomedical semantic annotators, namely cTAKES, MetaMap, NOBLE Coder, and Neji. The annotators were compared with respect to the quality of the produced annotations measured against gold and silver standards using precision, recall, and F1 measure and speed, i.e., processing time. In the experiments, RysannMD achieved the best median F1 measure across the benchmarking corpora, independent of the standard used (silver/gold), biomedical subdomain, and document size. In terms of the annotation speed, RysannMD scored the second best median processing time across all the experiments. The obtained results indicate that RysannMD offers the best performance among the examined semantic annotators when both quality of annotation and speed are considered simultaneously.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IMP2017,

title = {Self-Training on Refined Clause Patterns for Relation Extraction},

author = {Duc-Thuan Vo and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/j-ipm-2017-02-009-1/},

doi = {https://doi.org/10.1016/j.ipm.2017.02.009},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Information Processing and Management},

abstract = {Within the context of Information Extraction, relation extraction is oriented toward identifying a variety of relation phrases and their arguments in arbitrary sentences. In this paper, we present a clause-based framework for information extraction in textual documents. Our framework focuses on two important challenges in information extraction: 1) Open Information Extraction and (OIE) 2) Relation Extraction (RE). In the plethora of research that focus on the use of syntactic and dependency parsing for the purposes of detecting relations, there has been increasing evidence of incoherent and uninformative extractions. The extracted relations may even be erroneous at times and fail to provide a meaningful interpretation. In our work, we use the English clause structure and clause types in an effort to generate propositions that can be deemed as extractable relations. Moreover, we propose refinements to the grammatical structure of syntactic and dependency parsing that helps reduce the number of incoherent and uninformative extractions from clauses. In our experiments both in the open information extraction and relation extraction domains, we carefully evaluate our system on various benchmark datasets and compare the performance of our work against existing state-of-the-art information extraction systems. Our work shows improved performance compared to the state of the art techniques.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jbsm2017,

title = {Semantic Annotation in Biomedicine: The Current Landscape},

author = {Jelena Jovanovic and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/s13326-017-0153-x/},

doi = {https://doi.org/10.1186/s13326-017-0153-x},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Journal of Biomedical Semantics},

abstract = {The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates the advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS, so that the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators. Over the last dozen years, biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today's annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ker2017,

title = {The State of the Art in Semantic Relatedness: A Framework for Comparison},

author = {Yue Feng and Ebrahim Bagheri and Faezeh Ensan and Jelena Jovanovic},

url = {https://ls3.rnet.torontomu.ca/s0269888917000029/},

doi = {https://doi.org/10.1017/S0269888917000029},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {The Knowledge Engineering Review},

abstract = {Semantic relatedness (SR) is a form of measurement that quantitatively identifies the relationship between two words or concepts based on the similarity or closeness of their meaning. In the recent years, there have been noteworthy efforts to compute semantic relatedness between pairs of words or concepts by exploiting various knowledge resources such as linguistically-structured（e.g., WordNet) and collaboratively-developed knowledge bases (e.g., Wikipedia), among others. The existing approaches rely on different methods for utilizing these knowledge resources, for instance, methods that depend on the path between two words, or a vector representation of the word descriptions. The purpose of this paper is to review and present the state of the art in semantic relatedness research through a hierarchical framework. The dimensions of the proposed framework cover three main aspects of semantic relatedness approaches including the resources they rely on, the computational methods applied on the resources for developing a relatedness metric, and the evaluation models that are used for measuring their effectiveness. We have selected fourteen representative SR approaches to be analyzed using our framework. We compare and critically review each of them through the dimensions of our framework , thus, identifying strengths and weaknesses of each approach. In addition, we provide guidelines for researchers and practitioners on how to select the most relevant SR method for their purpose. Finally, based on the comparative analysis of the reviewed relatedness measures, we identify existing challenges and potentially valuable future research directions in this domain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{JSME2017,

title = {Toward Automated Quality-centric Product Line Configuration using Intentional Variability journal = Journal of Software: Evolution and Process},

author = {Mahdi Noorian and Ebrahim Bagheri and Weichang Du},

url = {http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2047-7481 abstract = Software Product Line Engineering (SPLE) is a discipline which facilitates a systematic reuse-basedapproach by formally representing commonalities and variabilities between the applications of a target domain. As one of the main artifact of the software product line, a feature model represents the possible configuration space and can be customized based on the stakeholders’ needs. Considering the complexity of the variabilities represented by feature models and the diversity of the stakeholders’ expectations, the configuration process can be viewed as a complex optimization problem. In previous research, researchers have bridged the gap between requirement and product line engineering by integrating feature models and goal models. In this paper, we propose an approach for the configuration process that seeks to satisfy the stakeholders’ requirements as well as the feature models’ structural and integrity constraints. We model stakeholders’ functional and non-functional needs and their preferences using requirement engineering goal models. We formalize the structure of the feature model, the stakeholders’ objectives, and their preferences in the form of an Integer Linear Program in order to conduct a semi automated feature model configuration process. Our experimental results show that the proposed configuration framework is scalable when considering both functional and non-functional requirements of stakeholders},

year  = {2017},

date = {2017-01-01},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{eswa2016,

title = {Efficient Indexing for Semantic Search},

author = {Fatemeh Lashkari and Faezeh Ensan and Ebrahim Bagheri and Ali A Ghorbani},

url = {http://www.journals.elsevier.com/expert-systems-with-applications/},

year  = {2016},

date = {2016-01-01},

journal = {Expert Systems With Applications},

abstract = {The increasing performance and wider spread use of automated semantic annotation and entity linking platforms has empowered the possibility of using semantic information in information retrieval. While keyword-based information retrieval techniques have shown impressive performance, the addition of semantic information can increase retrieval performance by allowing for more accurate sense disambiguation, intent determination, and instance identification, just to name a few. Researchers have already delved into the possibility of integrating semantic information into practical search engines using a combination of techniques such as using graph databases, hybrid indices and adapted inverted indices, among others. One of the challenges with the efficient design of a search engine capable of considering semantic information is that it would need to be able to index information beyond the traditional information stored in inverted indices, including entity mentions and type relationships. The objective of our work in 

this paper is to investigate various ways in which different data structure types can be adopted to integrate three types of information including keywords, entities and types. We will systematically compare the performance of the different data structures for scenarios where i) the same data structure types are adopted for the three types of information, and ii) different data structure types are integrated for storing and retrieving the three different information types. We report our findings in terms of the performance of various query processing tasks such as Boolean and ranked intersection for the different indices and discuss which index type would be appropriate under different conditions for semantic search.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{isfi2016,

title = {Mining Variable Fragments from Process Event Logs},

author = {Asef Pourmasoumi and Mohsen Kahani and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/s10796-016-9662-x-1/},

doi = {https://doi.org/10.1007/s10796-016-9662-x},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Information Systems Frontiers},

abstract = {Many peer-organizations are now using process-aware information systems for 

managing their organizational processes. Most of these peer-organizations have 

shared processes, which include many commonalities and some degrees of variability. 

Analyzing and mining the commonalities of these processes can have many benefits 

from the reusability point of view. In this paper, we propose an approach for extracting 

common process fragments from a collection of event logs. To this end, we first 

analyze the process fragment literature from a theo≈retical point of view, based on 

which we present a new process fragment definition, called morphological fragments to 

support composability and flexibility. Then we propose a novel algorithm for extracting 

such morphological fragments directly from process event logs. This algorithm is 

capable of eliciting common fragments from a family of processes that may not have 

been executed within the same application/organization. We also propose supporting 

algorithms for detecting and categorizing morphological fragments for the purpose of 

reusability. Our empirical studies show that our approach is able to support reusability 

and flexibility in process fragment identification.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{itpro2016,

title = {Overview of Text Annotation with Pictures},

author = {Kent Poots and Ebrahim Bagheri},

url = {http://www.computer.org/web/computingnow/itpro},

year  = {2016},

date = {2016-01-01},

journal = {IEEE IT Professional},

abstract = {The vast array of information available on the Web makes it a challenge for readers to quickly browse through and decide about the importance and relevance of content. Interpreting large-volumes of data is particularly demanding for users with handheld devices in the social media and micro-blogging sphere. Various approaches address this challenge through text summarization, content ranking and personalized recommendation. We describe a family of techniques that help users understand text by automatically annotating text with pictures, referred to as text picturing . The objective is to find a set of pictures that cover the main concepts in a textual snippet. We provide an overview of text picturing, its constituent steps such as knowledge extraction, map ping, scene rendering, as well as application areas. We give a picturing-related literature overview, and list use-cases that offer IT professionals insight into how picturing techniques can be successfully incorporated into real world applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/ijsc/GhashghaeiBCGN16,

title = {Semantic Disambiguation and Linking of Quantitative Mentions in Textual  Content},

author = {Mehrnaz Ghashghaei and Ebrahim Bagheri and John Cuzzola and Ali A Ghorbani and Zeinab Noorian},

url = {https://ls3.rnet.torontomu.ca/de2f932b65a6afa313ab889104791cca39f7/},

doi = {http://dx.doi.org/10.1142/S1793351X16500021},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Int. J. Semantic Computing},

volume = {10},

number = {1},

pages = {121},

abstract = {Semantic annotation techniques provide the basis for linking textual content with concepts in well grounded knowledge bases. In spite of their many application areas, current semantic annotation systems have some limitations. One of the prominent limitations of such systems is that none of the existing semantic annotator systems are able to identify and disambiguate quantitative (numerical) content. In textual documents such as Web pages, specially technical contents, there are many quantitative information such as product specifications that need to be semantically qualified. In this paper, we propose an approach for annotating quantitative values in short textual content. In our approach, we identify numeric values in the text and link them to an existing property in a knowledge base. Based on this mapping, we are then able to find the concept that the property is associated with, whereby identifying both the concept and the specific property of that concept that the numeric value belongs to. Results obtained from the developed gold standard dataset show that the proposed automated semantic annotation platform is quite effective in detecting and disambiguating numerical content, and connecting them to associated properties on the external knowledge base. Our experiments show that our proposed approach is able to reach an accuracy of over 70% for semantically annotating quantitative content.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/ase/BagheriE16,

title = {Semantic tagging and linking of software engineering social content},

author = {Ebrahim Bagheri and Faezeh Ensan},

url = {https://ls3.rnet.torontomu.ca/s10515-014-0146-2/},

doi = { https://doi.org/10.1007/s10515-014-0146-2},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Autom. Softw. Eng.},

volume = {23},

number = {2},

pages = {147--190},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jsme15,

title = {A Systematic Review of Distributed Agile Software Engineering},

author = {Dragan Gasevic Buturab Rizvi Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/smr-1718/},

doi = {https://doi.org/10.1002/smr.1718},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Journal of Software: Evolution and Process},

volume = {27},

number = {10},

pages = {723-762},

abstract = {Context: The combination of Agile methods and distributed software development via remote teams represents an emerging approach to addressing the challenges such as late feedback, slow project timelines, and high cost, typically associated with software development projects. However, when projects are implemented using an Agile model with distributed human resources, there are a number of challenges that need to be considered and mitigated. 

Objectives: The objectives of our work are multifold. First, we would like to understand the reasons and conditions that lead to the adoption of distributed agile software engineering practices. Second, we would like to investigate and find out the most important risks that threaten a distributed agile software engineering approach and what mitigation strategies exist to address them. Finally, would like to highlight which of the available approaches among the existing agile methodologies have been successfully adopted by the community. We intend to solidify our findings by exploring the strength of the evidence that has been reported in the literature. 

Methods: We carried out a systematic literature review of Distributed Agile Software Engineering techniques and approaches reported from January 1, 2007 until September 31, 2012. The adopted method follows the well-established guidelines in the literature for conducting systematic literature reviews. 

Results: Sixty-three distinct studies were selected and analyzed according to the inclusion and exclusion criteria, which focused on identifying only those studies from the literature that had a significant empirical or experimental aspect to them. The results revealed a significant number of scenarios reported by the industry that documented challenges and solutions in a Distributed Agile setting. Communication, Coordination, Collaboration, and Cultural issues were listed as being the areas where challenges exist. 

Conclusions: This systematic literature review found time zone difference, knowledge of resources, lack of infrastructure, missing roles and responsibilities as being the primary challenges that needed to be addressed. In terms of solutions, most papers had recommended having a good infrastructure in place for communication, encouraging team members to engage in formal and informal communications, having more face-to-face visits, training human resources on DASE and organizational practices, policies, procedures, and utilizing tools to enhance the collaboration experience, Additionally, this research provides recommendations to help improve the current state of reporting findings and results in the Distributed Agile Software Engineering domain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{asoc2015,

title = {Automated Classification and Localization of Daily Deal Content from the Web},

author = {Ebrahim Bagheri Dragan Gasevic John Cuzzola Jelena Jovanovic},

url = {https://ls3.rnet.torontomu.ca/asc2015/},

doi = {https://doi.org/10.1016/j.asoc.2015.02.029},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Applied Soft Computing},

volume = {31},

pages = {241-256},

abstract = {Websites offering daily deal offers have received widespread attention from the end-users. The objective of such Websites is to provide time limited discounts on goods and services in the hope of enticing more customers to purchase such goods or services. The success of daily deal Websites has given rise to meta-level daily deal aggregator services that collect daily deal information from across the Web. Due to some of the unique characteristics of daily deal Websites such as high update frequency, time sensitivity, and lack of coherent information representation, many deal aggregators rely on human intervention to identify and extract deal information. In this paper, we propose an approach where daily deal information is identified, classified and properly segmented and localized. Our approach is based on a semi-supervised method that uses sentence-level features of daily deal information on a given Web page. Our work offers i) a set of computationally inexpensive discriminative features that are able to effectively distinguish Web pages that contain daily deal information; ii) the construction and systematic evaluation of machine learning techniques based on these features to automatically classify daily deal Web pages; and iii) the development of an accurate segmentation algorithm that is able to localize and extract individual deals from within a complex Web page. We have extensively evaluated our approach from different perspectives, the results of which show notable performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{THMS2015,

title = {Comprehension and Learning of Social Goals through Visualization},

author = {Jelena Jovanovic Ebrahim Bagheri and Dragan Gasevic},

url = {https://ls3.rnet.torontomu.ca/comprehension_and_learning_of_social_goa/},

doi = {https://doi.org/10.1109/THMS.2015.2419083},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {IEEE Transactions on Human-Machine Systems},

abstract = {The concept of social goals refers to organizational goals that are defined in an open and transparent manner; they serve as social objects that incite both formal and informal collaboration around shared interests/objectives. Our objective is to facilitate the comprehension of social goals and examine the role of social goals as scaffolds of social learning in an organization. To this end, we followed an approach based on the visualization of social goals and explored how different presentations of goals, specifically, faceted goal browsing, graph - based visualization and timeline - based visualization, contribute to the realization of the stated objective. To assess this approach we conducted a between subjects study where each participant performed a set of goal comprehension tasks with one of the examined presentations of goals. The study demonstrated that our visualizations of goals increase the accuracy of the overall comprehension of an organizations goals; this positive effect is also present when the comprehension of relationship-either explicit or implicit tie-among social goals is needed. The results also confirmed that our graph-based visualization of social goals could serve as a facilitator of social learning in an organization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{itpro2015,

title = {Electronic commerce meets the Semantic Web},

author = {Jelena Jovanovic and Ebrahim Bagheri},

url = {https://ls3.rnet.torontomu.ca/electronic_commerce_meets_the_semantic_web_compressed/},

doi = {https://doi.org/10.1109/MITP.2016.56},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {IEEE IT Professional},

abstract = {Today's online retailers are facing many challenges, some of which are related to the efficient and effective integration, use and ma 

intenance of product and customer data.Technologies that make e-commerce data machine comprehensible could greatly assist in overcoming e-commerce data management challenges. In this paper, we look into the intersection of Semantic Web technologies and B2C e-commerce, and explore the benefits that can be reaped by both online retailers and customers. In particular, we propose a systematic framework that highlights why and how the adoption of Semantic Web technologies can enhance B2C applications and platforms. The framework is intended primarily for e-commerce decision makers and practitioners, to help them make more informed decisions on how to address e-commerce data management challenges using Semantic Web Technologies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{eswa15,

title = {Evolutionary Fine-Tuning of Automated Semantic Annotation Systems},

author = {Ebrahim Bagheri Dragan Gasevic John Cuzzola Jelena Jovanovic},

url = {https://ls3.rnet.torontomu.ca/j-eswa-2015-04-054/},

doi = {https://doi.org/10.1016/j.eswa.2015.04.054},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Expert Systems with Applications},

volume = {42},

number = {20},

pages = {6864–6877},

abstract = {Considering the ever-increasing speed at which new textual content is generated, the efficient and effective use of large text corpora requires automated natural language processing and text analysis tools. A subset of such tools, namely automated semantic annotation tools, are capable of interlinking syntactical forms of text with their underlying semantic concepts. The performance of semantic annotation tools depends on the characteristics of the annotation task, primarily characteristics of the text to be annotated, but also some other task-related features, such as the acceptable precision/recall trade-off. Therefore, these tools need to be optimally configured in order to give their best results when applied to different annotation tasks. However, the configuration of semantic annotators is presently a tedious and time-consuming task due to numerous potential configurations. In this paper, we propose an architecture and a genetic algorithm-based method for automating the task of configuring parameter values of any automated semantic annotation tool. This is a novel and unique solution as, to our knowledge, no solution to the problem of configuring semantic annotators has been reported in the literature so far. Our experiments demonstrate our proposed work enables effective configuration of parameters of any semantic annotator system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IJSEKE2014,

title = {Addressing Non-Functional Properties in Feature Models: A Goal-Oriented Approach},

author = {Mohsen Asadi Weichang Du Mahdi Noorian Ebrahim Bagheri},

year  = {2014},

date = {2014-01-01},

journal = {International Journal of Software Engineering and Knowledge Engineering},

volume = {24},

number = {10},

pages = {1439-1488},

abstract = {Software Product Line (SPL) engineering is a systematic reuse-based software development approach which is founded on the idea of building software products using a set of core assets rather than developing individual software systems from scratch. Feature models are among the widely used artefacts for SPL development that mostly capture functional and operational variability of a system. Researchers have argued that connecting intentional variability models such as goal models with feature variability models in a target domain can enrich feature models with valuable quality and non-functional information. Interrelating goal models and feature models has already been proposed in the literature for capturing non-functional properties in software product lines; however this manual integration process is cumbersome and tedious. In this paper, we propose a (semi) automated approach that systematically integrates feature models and goal models through standard ontologies. Our proposed approach connects feature model and goal model elements through measuring the semantic similarity of their annotated ontological concepts. Our work not only provides the means to systematically interrelate feature models and goal models but also allows domain engineers to identify and model the role and signicance of non-functional properties in the domain represented by the feature model.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ITPRO2014,

title = {Automated Semantic Tagging of Textual Content},

author = {Jelena Jovanovic and Ebrahim Bagheri and John Cuzzola and Dragan Gasevic and Zoran Jeremic and Reza Bashash},

year  = {2014},

date = {2014-01-01},

journal = {IEEE IT Professional},

volume = {16},

number = {6},

pages = {38-46},

abstract = {Motivated by a continually increasing demand for applications that depend on machine comprehension of text-based content, researchers, in both academia and industry, have developed innovative solutions for automated information extraction from text. In this article, we focus on a subset of such tools - i.e., semantic taggers - that not only extract and disambiguate entities mentioned in the text, but also identify topics that unambiguously describe the text's main themes. We offer insight into the process of semantic tagging, the capabilities and specificities of today's semantic taggers, and also indicate some of the criteria to be considered when choosing a tagger to use.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/REJ/BagheriE13,

title = {Dynamic Decision Models for Staged Software Product Line Configuration},

author = {Ebrahim Bagheri and Faezeh Ensan},

year  = {2014},

date = {2014-01-01},

journal = {Requirements Engineering Journal},

volume = {19},

number = {2},

pages = {187-212},

abstract = {Software product line engineering practices offer desirable characteristics such as rapid product development, reduced time-to-market and more affordable development costs as a result of systematic representation of the variabilities of a domain of discourse that leads to methodical reuse of software assets. The development lifecycle of a product line consists of two main phases: domain engineering, which deals with the understanding and formally modeling of the target domain; and application engineering that is concerned with the configuration of a product line into one concrete product based on the preferences and requirements of the stakeholders. The work presented in this paper focuses on the application engineering phase and builds both the theoretical and technological tools to assist the stakeholders in (1) understanding the complex interactions of the features of a product line; (2) eliciting the utility of each feature for the stakeholders and hence exposing the stakeholders' otherwise implicit preferences in a way that they can more easily make decisions; and (3) dynamically building a decision model through interaction with the stakeholders and by considering the structural characteristics of software product line feature models, which will guide the stakeholders through the product configuration process. Initial exploratory empirical experiments that we have performed show that our proposed approach for helping stakeholders understand their feature preferences and its associated staged feature model configuration process is able to positively impact the quality of the end results of the application engineering process within the context of the limited number of participants. In addition, it has been observed that the offered tooling support is able to ease the staged feature model configuration process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ijkss,

title = {Product Line Stakeholder Preference Elicitation via Decision Processes},

author = {Mahdi Bashari and Mahdi Noorian and Ebrahim Bagheri},

year  = {2014},

date = {2014-10-01},

journal = {International Journal of Knowledge and Systems Science},

volume = {5},

number = {4},

pages = {35-51},

abstract = {In the software product line configuration process, certain features are selected based on the stakeholders' needs and preferences regarding the available functional and quality properties. This book chapter presents how a product configuration can be modeled as a decision process and how an optimal strategy representing the stakeholders' desirable configuration can be found. In the decision process model of product configuration, the product is configured by making decisions at a number of decision points. The decisions at each of these decision points contribute to functional and quality attributes of the final product. In order to find an optimal strategy for the decision process, a utility-based approach can be adopted, through which, the strategy with the highest utility is selected as the optimal strategy. In order to define utility for each strategy, a multi-attribute utility function is defined over functional and quality properties of a configured product and a utility elicitation process is then introduced for finding this utility function. The utility elicitation process works based on asking gamble queries over functional and quality requirement from the stakeholder. Using this utility function, the optimal strategy and therefore optimal product configuration is determined.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CJECE2013,

title = {Reliability Estimation for Component-based Software Product Lines},

author = {Ebrahim Bagheri and Faezeh Ensan},

url = {10.1109/CJECE.2014.2323958},

year  = {2014},

date = {2014-01-01},

journal = {IEEE/Canadian Journal of Electrical and Computer Engineering},

volume = {37},

number = {2},

pages = {94--112},

abstract = {The objective of the software product line engineering paradigm is to enhance the large-scale reuse of common core assets within a target domain. Reuse is faciliated by systematically organizing and modeling the core assets and the relationships between them. One of the main core assets of a domain is the model for representing the available functional aspects, often known as emphfeatures, within structured forms such as feature models. The selection and composition of the most suitable or desirable set of features for a given purpose allows the rapid development of new final products from the software product line. Product developers are in most cases not only interested in building applications that possess certain functional characteristics but also concerned with non-functional properties of the final product such as textitreliability. To this end, we propose a component-based software product line reliability estimation model that is able to provide lower and upper reliability bound guarantees for a software product line feature model, its specializations and configurations. Our model builds on top of the reliability of the individual features that are present in the product line and provides best and worst-case estimates. Our work is based on an essential and widely used assumption that features are implemented using self-contained software components or services whose reliability can be determined independently. We also propose reliability-aware configuration methods that ensure the satisfaction of both functional and reliability requirements during the application development process. We offer our observations and insight into the performance of our reliability estimation model and provide analysis of its advantages and shortcomings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}