02855nas a2200277 4500008004100000245005400041210005400095520195300149653003302102653002302135653003302158653001502191653001502206100002802221700003402249700001902283700002002302700001602322700002802338700003202366700002102398700001702419700002302436700002002459856009802479 2020 eng d00aMultimodal mental health analysis in social media0 aMultimodal mental health analysis in social media3 a

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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.

10aExplainable Machine Learning10aHypothesis Testing10aNational Language Processing10aPrediction10aRegression1 aYazdavar, Amir, Hossein1 aMahdavinejad, Mohammad, Saeid1 aBaja, Goonmeet1 aRomine, William1 aSheth, Amit1 aMonadjemi, Amir, Hassan1 aThirunarayan, Krishnaprasad1 aMeddar, John, M.1 aMyers, Annie1 aPathak, Jyotishman1 aHitzler, Pascal uhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&type=printable00569nas a2200145 4500008004100000245006500041210006400106100002800170700003400198700002000232700003200252700002300284700001600307856010000323 2018 eng d00aMental Health Analysis Via Social Media Data, IEEE ICHI 20180 aMental Health Analysis Via Social Media Data IEEE ICHI 20181 aYazdavar, Amir, Hossein1 aMahdavinejad, Mohammad, Saied1 aBajaj, Goonmeet1 aThirunarayan, Krishnaprasad1 aPathak, Jyotishman1 aSheth, Amit uhttps://daselab.cs.ksu.edu/publications/mental-health-analysis-social-media-data-ieee-ichi-201801585nas a2200145 4500008004100000245004900041210004700090520110000137100002101237700002801258700003201286700001601318700001601334856008901350 2017 eng d00aRelatedness-based Multi-Entity Summarization0 aRelatednessbased MultiEntity Summarization3 a

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’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.

1 aGunaratna, Kalpa1 aYazdavar, Amir, Hossein1 aThirunarayan, Krishnaprasad1 aSheth, Amit1 aCheng, Gong uhttps://daselab.cs.ksu.edu/publications/relatedness-based-multi-entity-summarization01762nas a2200181 4500008004100000245008800041210006900129520113900198100002801337700002701365700002201392700002001414700002001434700003201454700002301486700001601509856005501525 2017 eng d00aSemi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media0 aSemiSupervised Approach to Monitoring Clinical Depressive Sympto3 a

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%.

1 aYazdavar, Amir, Hossein1 aAl-Olimat, Hussein, S.1 aEbrahimi, Monireh1 aBajaj, Goonmeet1 aBanerjee, Tanvi1 aThirunarayan, Krishnaprasad1 aPathak, Jyotishman1 aSheth, Amit uhttps://dl.acm.org/doi/abs/10.1145/3110025.312302801183nas a2200289 4500008004100000245005900041210005600100260002500156300001000181490000900191520034600200100002200546700002100568700002300589700002400612700002000636700002000656700002200676700003200698700002100730700002100751700002000772700001800792700002400810700002700834856003200861 2014 eng d00aAn Ontology Design Pattern for Material Transformation0 aOntology Design Pattern for Material Transformation bCEUR-WS.orgc10/2014 a73-770 v13023 aIn 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.1 aVardeman, Charles1 aKrisnadhi, Adila1 aCheatham, Michelle1 aJanowicz, Krzysztof1 aFerguson, Holly1 aHitzler, Pascal1 aBuccellato, Aimee1 aThirunarayan, Krishnaprasad1 aBerg-Cross, Gary1 aHahmann, Torsten1 ade Boer, Victor1 aGangemi, Aldo1 aJanowicz, Krzysztof1 aLawrynowicz, Agnieszka uhttp://ceur-ws.org/Vol-130201489nas a2200181 4500008004100000245006100041210006000102260004800162490000800210520085700218100002101075700003201096700002001128700002001148700002201168700002201190856009501212 2011 eng d00aRepresentation of Parsimonious Covering Theory in OWL-DL0 aRepresentation of Parsimonious Covering Theory in OWLDL aSan Francisco, California, USAbCEUR-WS.org0 v7963 a

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.

1 aHenson, Cory, A.1 aThirunarayan, Krishnaprasad1 aSheth, Amit, P.1 aHitzler, Pascal1 aDumontier, Michel1 aCourtot, Mélanie uhttps://daselab.cs.ksu.edu/publications/representation-parsimonious-covering-theory-owl-dl02649nas a2200277 4500008004100000245009100041210006900132260003400201300001400235490000900249520174100258653003601999653001902035653003002054653003702084653002202121653002002143100002102163700002502184700002002209700001602229700003202245700001902277700002402296856005102320 2010 eng d00aProvenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data0 aProvenance Context Entity PaCE Scalable Provenance Tracking for  aHeidelberg, GermanybSpringer a461–4700 v61873 a

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. 

10aBiomedical knowledge repository10aContext theory10aProvenance context entity10aProvenance Management Framework.10aProvenir ontology10aRDF reification1 aSahoo, Satya, S.1 aBodenreider, Olivier1 aHitzler, Pascal1 aSheth, Amit1 aThirunarayan, Krishnaprasad1 aGertz, Michael1 aLudäscher, Bertram uhttp://dx.doi.org/10.1007/978-3-642-13818-8_32