@article {775, title = {Multimodal mental health analysis in social media}, journal = {PLoS ONE}, year = {2020}, abstract = {

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Depression is a major public health concern in the U.S. and globally. While successful early

identification and treatment can lead to many positive health and behavioral outcomes,

depression, remains undiagnosed, untreated or undertreated due to several reasons,

including denial of the illness as well as cultural and social stigma. With the ubiquity of social

media platforms, millions of people are now sharing their online persona by expressing their

thoughts, moods, emotions, and even their daily struggles with mental health on social

media. Unlike traditional observational cohort studies conducted through questionnaires

and self-reported surveys, we explore the reliable detection of depressive symptoms from

tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)

data to discern depressive behaviors using a wide variety of features including individuallevel

demographics. By developing a multimodal framework and employing statistical techniques

to fuse heterogeneous sets of features obtained through the processing of visual,

textual, and user interaction data, we significantly enhance the current state-of-the-art

approaches for identifying depressed individuals on Twitter (improving the average F1-

Score by 5 percent) as well as facilitate demographic inferences from social media. Besides

providing insights into the relationship between demographics and mental health, our

research assists in the design of a new breed of demographic-aware health interventions.

}, keywords = {Explainable Machine Learning, Hypothesis Testing, National Language Processing, Prediction, Regression}, url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248\&type=printable}, author = {Amir Hossein Yazdavar and Mohammad Saeid Mahdavinejad and Goonmeet Baja and William Romine and Amit Sheth and Amir Hassan Monadjemi and Krishnaprasad Thirunarayan and John M. Meddar and Annie Myers and Jyotishman Pathak and Pascal Hitzler} } @conference {777, title = {Mental Health Analysis Via Social Media Data, IEEE ICHI 2018}, booktitle = {IEEE, ICHI}, year = {2018}, author = {Amir Hossein Yazdavar and Mohammad Saied Mahdavinejad and Goonmeet Bajaj and Krishnaprasad Thirunarayan and Jyotishman Pathak and Amit Sheth} } @conference {778, title = {Relatedness-based Multi-Entity Summarization}, booktitle = {IJCAI}, year = {2017}, abstract = {

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple{\textquoteright}s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-ofthe-art entity summarization approaches.

}, author = {Kalpa Gunaratna and Amir Hossein Yazdavar and Krishnaprasad Thirunarayan and Amit Sheth and Gong Cheng} } @conference {776, title = {Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media}, booktitle = {ASONAM}, year = {2017}, abstract = {

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68\% and precision of 72\%.

}, url = {https://dl.acm.org/doi/abs/10.1145/3110025.3123028}, author = {Amir Hossein Yazdavar}, editor = {Hussein S. Al-Olimat and Monireh Ebrahimi and Goonmeet Bajaj and Tanvi Banerjee and Krishnaprasad Thirunarayan and Jyotishman Pathak and Amit Sheth} } @conference {146, title = {An Ontology Design Pattern for Material Transformation}, booktitle = {Proceedings of the 5th Workshop on Ontology and Semantic Web Patterns (WOP2014) co-located with the 13th International Semantic Web Conference (ISWC 2014), Riva del Garda, Italy, October 19, 2014.}, volume = {1302}, year = {2014}, month = {10/2014}, pages = {73-77}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {In this work we discuss an ontology design pattern for material transformations. It models the relation between products, resources, and catalysts in the transformation process. Our axiomatization goes beyond a mere surface semantics. While we focus on the construction domain, the pattern can also be applied to chemistry and other domains.}, url = {http://ceur-ws.org/Vol-1302}, author = {Charles Vardeman and Adila Krisnadhi and Michelle Cheatham and Krzysztof Janowicz and Holly Ferguson and Pascal Hitzler and Aimee Buccellato and Krishnaprasad Thirunarayan and Gary Berg-Cross and Torsten Hahmann}, editor = {de Boer, Victor and Aldo Gangemi and Krzysztof Janowicz and Agnieszka Lawrynowicz} } @conference {404, title = {Representation of Parsimonious Covering Theory in OWL-DL}, booktitle = {Proceedings of the 8th International Workshop on OWL: Experiences and Directions {(OWLED} 2011), San Francisco, California, USA, June 5-6, 2011}, volume = {796}, year = {2011}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {San Francisco, California, USA}, abstract = {

The Web Ontology Language has not been designed for representing abductive inference, which is often required for applications such as medical disease diagnosis. As a consequence, existing OWL ontologies have limited ability to encode knowledge for such applications. In the last 150 years, many logic frameworks for the representation of abductive inference have been developed. Among these frameworks, Parsimonious Covering Theory (PCT) has achieved wide recognition. PCT is a formal model of diagnostic reasoning in which knowledge is represented as a network of causal associations, and whose goal is to account for observed symptoms with plausible explanatory hypotheses. In this paper, we argue that OWL does provide some of the expressivity required to approximate diagnostic reasoning, and outline a suitable encoding of PCT in OWL-DL.

}, author = {Cory A. Henson and Krishnaprasad Thirunarayan and Amit P. Sheth and Pascal Hitzler}, editor = {Michel Dumontier and M{\'e}lanie Courtot} } @conference {112, title = {Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data}, booktitle = {Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010}, volume = {6187}, year = {2010}, pages = {461{\textendash}470}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg, Germany}, abstract = {

The Semantic Web Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current Semantic Web provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples that hinders data sharing. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses the notion of provenance context to create provenance-aware RDF triples without the use of RDF reification or blank nodes. We also define the formal semantics of PaCE through a simple extension of the existing RDF(S) semantics that ensures compatibility of PaCE with existing Semantic Web tools and implementations. We have implemented the PaCE approach in the Biomedical Knowledge Repository (BKR) project at the US National Library of Medicine to support provenance tracking on RDF data extracted from multiple sources, including biomedical literature and the UMLS Metathesaurus. The evaluations demonstrate a minimum of 49\% reduction in total number of provenancespecific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, using the PACE approach improves the performance of complex provenance queries by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries.\ 

}, keywords = {Biomedical knowledge repository, Context theory, Provenance context entity, Provenance Management Framework., Provenir ontology, RDF reification}, doi = {10.1007/978-3-642-13818-8_32}, url = {http://dx.doi.org/10.1007/978-3-642-13818-8_32}, author = {Satya S. Sahoo and Olivier Bodenreider and Pascal Hitzler and Amit Sheth and Krishnaprasad Thirunarayan}, editor = {Michael Gertz and Bertram Lud{\"a}scher} }