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-201801271nas a2200121 4500008004100000245005600041210005600097520083900153100002200992700002801014700001601042856009101058 2017 eng d00aChallenges of Sentiment Analysis for Dynamic Events0 aChallenges of Sentiment Analysis for Dynamic Events3 aEfforts to assess people's sentiments on Twitter have suggested that Twitter could be a valuable resource for studying political sentiment and that it reflects the offline political landscape. Many opinion mining systems and tools provide users with people's attitudes toward products, people, or topics and their attributes/aspects. However, although it may appear simple, using sentiment analysis to predict election results is difficult, since it is empirically challenging to train a successful model to conduct sentiment analysis on tweet streams for a dynamic event such as an election. This article highlights some of the challenges related to sentiment analysis encountered during monitoring of the presidential election using Kno.e.sis's Twitris system.
1 aEbrahimi, Monireh1 aYazdavar, Amir, Hossein1 aSheth, Amit uhttps://daselab.cs.ksu.edu/publications/challenges-sentiment-analysis-dynamic-events-001585nas a2200145 4500008004100000245004900041210004700090520110000137100002101237700002801258700003201286700001601318700001601334856008901350 2017 eng d00aRelatedness-based Multi-Entity Summarization0 aRelatednessbased MultiEntity Summarization3 aRepresenting 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 aWith 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/10.1145/3110025.312302801378nas a2200193 4500008004100000245005700041210005700098260002100155300001400176520079900190653001900989653002601008653002701034100002301061700002001084700001601104700001801120856004601138 2013 eng d00aAutomatic Domain Identification for Linked Open Data0 aAutomatic Domain Identification for Linked Open Data aAtlanta, GA, USA a205–2123 aLinked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.
10adataset search10aDomain Identification10aLinked Open Data Cloud1 aLalithsena, Sarasi1 aHitzler, Pascal1 aSheth, Amit1 aJain, Prateek uhttp://dx.doi.org/10.1109/WI-IAT.2013.20600568nas a2200181 4500008004100000245004900041210004800090260001300138300001400151100002500165700001800190700002000208700001900228700001700247700001600264700002000280856008600300 2012 eng d00aAlignment-based querying of linked open data0 aAlignmentbased querying of linked open data bSpringer a807–8241 aJoshi, Amit, Krishna1 aJain, Prateek1 aHitzler, Pascal1 aYeh, Peter, Z.1 aVerma, Kunal1 aSheth, Amit1 aDamova, Mariana uhttps://daselab.cs.ksu.edu/publications/alignment-based-querying-linked-open-data01920nas a2200229 4500008004100000245007300041210006900114260002800183300001200211520122300223653002701446653001401473653002101487100001801508700002001526700001701546700001901563700001601582700002201598700002301620856004701643 2012 eng d00aMoving beyond SameAs with PLATO: Partonomy detection for Linked Data0 aMoving beyond SameAs with PLATO Partonomy detection for Linked D aMilwaukee, WI, USAbACM a33–423 aThe Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery.
Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications.
In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.
10aLinked Open Data Cloud10aMereology10aPart of Relation1 aJain, Prateek1 aHitzler, Pascal1 aVerma, Kunal1 aYeh, Peter, Z.1 aSheth, Amit1 aMunson, Ethan, V.1 aStrohmaier, Markus uhttp://doi.acm.org/10.1145/2309996.231000401201nas a2200169 4500008004100000245003400041210003400075520071900109100002000828700002400848700002100872700001500893700001600908700001900924700001700943856007100960 2012 eng d00aSemantic Aspects of EarthCube0 aSemantic Aspects of EarthCube3 aIn this document, we give a high-level overview of selected Semantic (Web) technologies, methods, and other important considerations, that are relevant for the success of EarthCube. The goal of this initial document is to provide entry points and references for discussions between the Semantic Technologies experts and the domain experts within EarthCube. The selected topics are intended to ground the EarthCube roadmap in the state of the art in semantics research and ontology engineering.
We anticipate that this document will evolve as EarthCube progresses. Indeed, all EarthCube parties are asked to provide topics of importance that should be treated in future versions of this document.
1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aBerg-Cross, Gary1 aObrst, Leo1 aSheth, Amit1 aFinin, Timothy1 aCruz, Isabel uhttps://daselab.cs.ksu.edu/publications/semantic-aspects-earthcube02034nas a2200373 4500008004100000245004300041210004300084260002400127520100800151100002101159700001701180700001501197700001501212700001801227700002001245700001401265700002401279700001601303700001901319700002001338700001501358700002201373700001601395700001901411700001801430700001901448700002001467700002401487700001601511700001801527700001601545700002001561856007901581 2012 eng d00aSemantics and Ontologies for EarthCube0 aSemantics and Ontologies for EarthCube aColumbus, Ohio, USA3 aSemantic technologies and ontologies play an increasing role in scientific workflow systems and knowledge infrastructures. While ontologies are mostly used for the semantic annotation of metadata, semantic technologies enable searching metadata catalogs beyond simple keywords, with some early evidence of semantics used for data translation. However, the next generation of distributed and interdisciplinary knowledge infrastructures will require capabilities beyond simple subsumption reasoning over subclass relations. In this work, we report from the EarthCube Semantics Community by highlighting which role semantics and ontologies should play in the EarthCube knowledge infrastructure. We target the interested domain scientist and, thus, introduce the value proposition of semantic technologies in a non-technical language. Finally, we commit ourselves to some guiding principles for the successful implementation and application of semantic technologies and ontologies within EarthCube.
1 aBerg-Cross, Gary1 aCruz, Isabel1 aDean, Mike1 aFinin, Tim1 aGahegan, Mark1 aHitzler, Pascal1 aHua, Hook1 aJanowicz, Krzysztof1 aLi, Naicong1 aMurphy, Philip1 aNordgren, Bryce1 aObrst, Leo1 aSchildhauer, Mark1 aSheth, Amit1 aSinha, Krishna1 aThessen, Anne1 aWiegand, Nancy1 aZaslavsky, Ilya1 aJanowicz, Krzysztof1 aKessler, C.1 aKauppinen, T.1 aKolas, Dave1 aScheider, Simon uhttps://daselab.cs.ksu.edu/publications/semantics-and-ontologies-earthcube02232nas a2200289 4500008004100000245009000041210006900131260003900200300001200239490000900251520133400260100001801594700001901612700001701631700002701648700002001675700002001695700001601715700002301731700002101754700003101775700001801806700002601824700002401850700001801874856005001892 2011 eng d00aContextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton0 aContextual Ontology Alignment of LOD with an Upper Ontology A Ca aHeraklion, Crete, GreecebSpringer a80–920 v66433 aThe Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies – in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a more sophisticated metric to determine which classes between two ontologies to align, and 2) considers contextual information to further support (or reject) an alignment. We present a comprehensive evaluation of our solution using schema-level mappings from LOD ontologies to Proton (an upper level ontology) – created manually by human experts for a real world application called FactForge. We show that our solution performed well on this task. We also show that our solution significantly outperformed existing ontology alignment solutions (including our previously published work on BLOOMS) on this same task.
1 aJain, Prateek1 aYeh, Peter, Z.1 aVerma, Kunal1 aVasquez, Reymonrod, G.1 aDamova, Mariana1 aHitzler, Pascal1 aSheth, Amit1 aAntoniou, Grigoris1 aGrobelnik, Marko1 aSimperl, Elena, Paslaru Bo1 aParsia, Bijan1 aPlexousakis, Dimitris1 aDe Leenheer, Pieter1 aPan, Jeff, Z. uhttp://dx.doi.org/10.1007/978-3-642-21034-1_601035nas a2200205 4500008004100000245003600041210003600077260003600113520044200149100001800591700002000609700001900629700001700648700001600665700001800681700002400699700001800723700002400741856006400765 2010 eng d00aLinked Data Is Merely More Data0 aLinked Data Is Merely More Data aStanford, California, USAbAAAI3 aIn this position paper, we argue that the Linked Open Data (LoD) Cloud, in its current form, is only of limited value for furthering the Semantic Web vision. Being merely a weakly linked “triple collection,” it will only be of very limited bene- fit for the AI or Semantic Web communities. We describe the corresponding problems with the LoD Cloud and give directions for research to remedy the situation.
1 aJain, Prateek1 aHitzler, Pascal1 aYeh, Peter, Z.1 aVerma, Kunal1 aSheth, Amit1 aBrickley, Dan1 aChaudhri, Vinay, K.1 aHalpin, Harry1 aMcGuinness, Deborah uhttp://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/113001773nas a2200277 4500008004100000245004400041210004400085260003000129300001400159490000900173520102400182100001801206700002001224700001601244700001701260700001901277700003101296700001301327700002001340700001601360700001501376700001801391700001801409700001701427856005101444 2010 eng d00aOntology Alignment for Linked Open Data0 aOntology Alignment for Linked Open Data aShanghai, ChinabSpringer a402–4170 v64963 aThe Web of Data currently coming into existence through the Linked Open Data (LOD) effort is a major milestone in realizing the Semantic Web vision. However, the development of applications based on LOD faces difficulties due to the fact that the different LOD datasets are rather loosely connected pieces of information. In particular, links between LOD datasets are almost exclusively on the level of instances, and schema-level information is being ignored. In this paper, we therefore present a system for finding schema-level links between LOD datasets in the sense of ontology alignment. Our system, called BLOOMS, is based on the idea of bootstrapping information already present on the LOD cloud. We also present a comprehensive evaluation which shows that BLOOMS outperforms state-of-the-art ontology alignment systems on LOD datasets. At the same time, BLOOMS is also competitive compared with these other systems on the Ontology Evaluation Alignment Initiative Benchmark datasets.
1 aJain, Prateek1 aHitzler, Pascal1 aSheth, Amit1 aVerma, Kunal1 aYeh, Peter, Z.1 aPatel-Schneider, Peter, F.1 aPan, Yue1 aHitzler, Pascal1 aMika, Peter1 aZhang, Lei1 aPan, Jeff, Z.1 aHorrocks, Ian1 aGlimm, Birte uhttp://dx.doi.org/10.1007/978-3-642-17746-0_2602649nas 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 aThe 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_3200588nas a2200169 4500008004100000245005900041210005900100260002300159100002300182700002100205700002100226700002300247700001700270700001600287700001900303856009600322 2009 eng d00aOntology Driven Integration of Biology Experiment Data0 aOntology Driven Integration of Biology Experiment Data aCleveland, OH, USA1 aMutharaju, Raghava1 aSahoo, Satya, S.1 aWeatherly, Brent1 aAnantharam, Pramod1 aLogan, Flora1 aSheth, Amit1 aTarleton, Rick uhttps://daselab.cs.ksu.edu/publications/ontology-driven-integration-biology-experiment-data02427nas a2200229 4500008004100000245009100041210006900132260003400201300001500235490000900250520170100259100002101960700002101981700002302002700002302025700001602048700001902064700002102083700002302104700001902127856005102146 2009 eng d00aOntology-Driven Provenance Management in eScience: An Application in Parasite Research0 aOntologyDriven Provenance Management in eScience An Application aVilamoura, PortugalbSpringer a992–10090 v58713 aProvenance, from the French word “provenir”, describes the lineage or history of a data entity. Provenance is critical information in scientific applications to verify experiment process, validate data quality and associate trust values with scientific results. Current industrial scale eScience projects require an end-to-end provenance management infrastructure. This infrastructure needs to be underpinned by formal semantics to enable analysis of large scale provenance information by software applications. Further, effective analysis of provenance information requires well-defined query mechanisms to support complex queries over large datasets. This paper introduces an ontology-driven provenance management infrastructure for biology experiment data, as part of the Semantic Problem Solving Environment (SPSE) for Trypanosoma cruzi (T.cruzi). This provenance infrastructure, called T.cruzi Provenance Management System (PMS), is underpinned by (a) a domain-specific provenance ontology called Parasite Experiment ontology, (b) specialized query operators for provenance analysis, and (c) a provenance query engine. The query engine uses a novel optimization technique based on materialized views called materialized provenance views (MPV) to scale with increasing data size and query complexity. This comprehensive ontology-driven provenance infrastructure not only allows effective tracking and management of ongoing experiments in the Tarleton Research Group at the Center for Tropical and Emerging Global Diseases (CTEGD), but also enables researchers to retrieve the complete provenance information of scientific results for publication in literature.
1 aSahoo, Satya, S.1 aWeatherly, Brent1 aMutharaju, Raghava1 aAnantharam, Pramod1 aSheth, Amit1 aTarleton, Rick1 aMeersman, Robert1 aDillon, Tharam, S.1 aHerrero, Pilar uhttp://dx.doi.org/10.1007/978-3-642-05151-7_1801251nas a2200229 4500008004100000245008900041210006900130260002900199300001400228490000900242520052600251100002500777700002100802700001600823700002000839700002300859700002100882700002200903700002500925700002000950856005100970 2009 eng d00aSpatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences0 aSpatioTemporalThematic Analysis of Citizen Sensor Data Challenge aPoznan, PolandbSpringer a539–5530 v58023 aWe present work in the spatio-temporal-thematic analysis of citizen-sensor observations pertaining to real-world events. Using Twitter as a platform for obtaining crowd-sourced observations, we explore the interplay between these 3 dimensions in extracting insightful summaries of social perceptions behind events. We present our experiences in building a web mashup application, Twitris [1] that extracts and facilitates the spatio-temporal-thematic exploration of event descriptor summaries.
1 aNagarajan, Meenakshi1 aGomadam, Karthik1 aSheth, Amit1 aRanabahu, Ajith1 aMutharaju, Raghava1 aJadhav, Ashutosh1 aVossen, Gottfried1 aLong, Darrell, D. E.1 aYu, Jeffrey, Xu uhttp://dx.doi.org/10.1007/978-3-642-04409-0_5200638nas a2200169 4500008004100000245007800041210006900119260002300188100002300211700002100234700002100255700001700276700002300293700001600316700001900332856011700351 2009 eng d00aTrykipedia: Collaborative Bio-Ontology Development using Wiki Environment0 aTrykipedia Collaborative BioOntology Development using Wiki Envi aCleveland, OH, USA1 aAnantharam, Pramod1 aSahoo, Satya, S.1 aWeatherly, Brent1 aLogan, Flora1 aMutharaju, Raghava1 aSheth, Amit1 aTarleton, Rick uhttps://daselab.cs.ksu.edu/publications/trykipedia-collaborative-bio-ontology-development-using-wiki-environment01167nas a2200205 4500008004100000245004200041210004100083260002300124520055100147100002100698700001600719700002300735700002300758700001700781700001600798700002100814700002500835700002000860856008100880 2009 eng d00aTwitris: Socially Influenced Browsing0 aTwitris Socially Influenced Browsing aWashington DC, USA3 aIn this paper, we present Twitris, a semantic Web application that facilitates browsing for news and information, using social perceptions as the fulcrum. In doing so we address challenges in large scale crawling, processing of real time information, and preserving spatiotemporal-thematic properties central to observations pertaining to realtime events. We extract metadata about events from Twitter and bring related news and Wikipedia articles to the user. In developing Twitris, we have used the DBPedia ontology.
1 aJadhav, Ashutosh1 aWang, Wenbo1 aMutharaju, Raghava1 aAnantharam, Pramod1 aNguyen, Vinh1 aSheth, Amit1 aGomadam, Karthik1 aNagarajan, Meenakshi1 aRanabahu, Ajith uhttps://daselab.cs.ksu.edu/publications/twitris-socially-influenced-browsing