TY - JOUR T1 - Multimodal mental health analysis in social media JF - PLoS ONE Y1 - 2020 A1 - Amir Hossein Yazdavar A1 - Mohammad Saeid Mahdavinejad A1 - Goonmeet Baja A1 - William Romine A1 - Amit Sheth A1 - Amir Hassan Monadjemi A1 - Krishnaprasad Thirunarayan A1 - John M. Meddar A1 - Annie Myers A1 - Jyotishman Pathak A1 - Pascal Hitzler KW - Explainable Machine Learning KW - Hypothesis Testing KW - National Language Processing KW - Prediction KW - Regression AB -

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}

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.

UR - https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&type=printable ER - TY - CONF T1 - Mental Health Analysis Via Social Media Data, IEEE ICHI 2018 T2 - IEEE, ICHI Y1 - 2018 A1 - Amir Hossein Yazdavar A1 - Mohammad Saied Mahdavinejad A1 - Goonmeet Bajaj A1 - Krishnaprasad Thirunarayan A1 - Jyotishman Pathak A1 - Amit Sheth JF - IEEE, ICHI ER - TY - Generic T1 - Challenges of Sentiment Analysis for Dynamic Events Y1 - 2017 A1 - Monireh Ebrahimi A1 - Amir Hossein Yazdavar A1 - Amit Sheth AB -

Efforts 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.

ER - TY - CONF T1 - Relatedness-based Multi-Entity Summarization T2 - IJCAI Y1 - 2017 A1 - Kalpa Gunaratna A1 - Amir Hossein Yazdavar A1 - Krishnaprasad Thirunarayan A1 - Amit Sheth A1 - Gong Cheng AB -

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.

JF - IJCAI ER - TY - CONF T1 - Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media T2 - ASONAM Y1 - 2017 A1 - Amir Hossein Yazdavar ED - Hussein S. Al-Olimat ED - Monireh Ebrahimi ED - Goonmeet Bajaj ED - Tanvi Banerjee ED - Krishnaprasad Thirunarayan ED - Jyotishman Pathak ED - Amit Sheth AB -

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%.

JF - ASONAM UR - https://dl.acm.org/doi/abs/10.1145/3110025.3123028 ER - TY - CONF T1 - Automatic Domain Identification for Linked Open Data T2 - 2013 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2013 Y1 - 2013 A1 - Sarasi Lalithsena A1 - Pascal Hitzler A1 - Amit Sheth A1 - Prateek Jain KW - dataset search KW - Domain Identification KW - Linked Open Data Cloud AB -

Linked 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.

JF - 2013 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2013 CY - Atlanta, GA, USA UR - http://dx.doi.org/10.1109/WI-IAT.2013.206 ER - TY - CHAP T1 - Alignment-based querying of linked open data T2 - On the Move to Meaningful Internet Systems: OTM 2012 Y1 - 2012 A1 - Joshi, Amit Krishna A1 - Prateek Jain A1 - Pascal Hitzler A1 - Peter Z. Yeh A1 - Kunal Verma A1 - Amit Sheth A1 - Mariana Damova JF - On the Move to Meaningful Internet Systems: OTM 2012 PB - Springer ER - TY - CONF T1 - Moving beyond SameAs with PLATO: Partonomy detection for Linked Data T2 - 23rd ACM Conference on Hypertext and Social Media, HT '12 Y1 - 2012 A1 - Prateek Jain A1 - Pascal Hitzler A1 - Kunal Verma A1 - Peter Z. Yeh A1 - Amit Sheth ED - Ethan V. Munson ED - Markus Strohmaier KW - Linked Open Data Cloud KW - Mereology KW - Part of Relation AB -

The 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.

JF - 23rd ACM Conference on Hypertext and Social Media, HT '12 PB - ACM CY - Milwaukee, WI, USA UR - http://doi.acm.org/10.1145/2309996.2310004 ER - TY - RPRT T1 - Semantic Aspects of EarthCube Y1 - 2012 A1 - Pascal Hitzler A1 - Krzysztof Janowicz A1 - Gary Berg-Cross A1 - Leo Obrst A1 - Amit Sheth A1 - Timothy Finin A1 - Isabel Cruz AB -

In 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.

JF - EarthCube report of the Technology Subcommittee of the EarthCube Semantics and Ontologies Group ER - TY - CONF T1 - Semantics and Ontologies for EarthCube T2 - Workshop on GIScience in the Big Data Age, In conjunction with the seventh International Conference on Geographic Information Science 2012 (GIScience 2012) Y1 - 2012 A1 - Gary Berg-Cross A1 - Isabel Cruz A1 - Mike Dean A1 - Tim Finin A1 - Mark Gahegan A1 - Pascal Hitzler A1 - Hook Hua A1 - Krzysztof Janowicz A1 - Naicong Li A1 - Philip Murphy A1 - Bryce Nordgren A1 - Leo Obrst A1 - Mark Schildhauer A1 - Amit Sheth A1 - Krishna Sinha A1 - Anne Thessen A1 - Nancy Wiegand A1 - Ilya Zaslavsky ED - Krzysztof Janowicz ED - C. Kessler ED - T. Kauppinen ED - Dave Kolas ED - Simon Scheider AB -

Semantic 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.

JF - Workshop on GIScience in the Big Data Age, In conjunction with the seventh International Conference on Geographic Information Science 2012 (GIScience 2012) CY - Columbus, Ohio, USA ER - TY - CONF T1 - Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton T2 - The Semantic Web: Research and Applications - 8th Extended Semantic Web Conference, ESWC 2011 Y1 - 2011 A1 - Prateek Jain A1 - Peter Z. Yeh A1 - Kunal Verma A1 - Reymonrod G. Vasquez A1 - Mariana Damova A1 - Pascal Hitzler A1 - Amit Sheth ED - Grigoris Antoniou ED - Marko Grobelnik ED - Elena Paslaru Bontas Simperl ED - Bijan Parsia ED - Dimitris Plexousakis ED - Pieter De Leenheer ED - Jeff Z. Pan AB -

The 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.

JF - The Semantic Web: Research and Applications - 8th Extended Semantic Web Conference, ESWC 2011 PB - Springer CY - Heraklion, Crete, Greece VL - 6643 UR - http://dx.doi.org/10.1007/978-3-642-21034-1_6 ER - TY - CONF T1 - Linked Data Is Merely More Data T2 - Linked Data Meets Artificial Intelligence, Papers from the 2010 AAAI Spring Symposium Y1 - 2010 A1 - Prateek Jain A1 - Pascal Hitzler A1 - Peter Z. Yeh A1 - Kunal Verma A1 - Amit Sheth ED - Dan Brickley ED - Vinay K. Chaudhri ED - Harry Halpin ED - Deborah McGuinness AB -

In 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.

JF - Linked Data Meets Artificial Intelligence, Papers from the 2010 AAAI Spring Symposium PB - AAAI CY - Stanford, California, USA UR - http://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/1130 ER - TY - CONF T1 - Ontology Alignment for Linked Open Data T2 - The Semantic Web - ISWC 2010 - 9th International Semantic Web Conference, ISWC 2010 Y1 - 2010 A1 - Prateek Jain A1 - Pascal Hitzler A1 - Amit Sheth A1 - Kunal Verma A1 - Peter Z. Yeh ED - Peter F. Patel-Schneider ED - Yue Pan ED - Pascal Hitzler ED - Peter Mika ED - Lei Zhang ED - Jeff Z. Pan ED - Ian Horrocks ED - Birte Glimm AB -

The 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.

JF - The Semantic Web - ISWC 2010 - 9th International Semantic Web Conference, ISWC 2010 PB - Springer CY - Shanghai, China VL - 6496 UR - http://dx.doi.org/10.1007/978-3-642-17746-0_26 ER - TY - CONF T1 - Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data T2 - Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010 Y1 - 2010 A1 - Satya S. Sahoo A1 - Olivier Bodenreider A1 - Pascal Hitzler A1 - Amit Sheth A1 - Krishnaprasad Thirunarayan ED - Michael Gertz ED - Bertram Ludäscher KW - Biomedical knowledge repository KW - Context theory KW - Provenance context entity KW - Provenance Management Framework. KW - Provenir ontology KW - RDF reification AB -

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. 

JF - Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010 PB - Springer CY - Heidelberg, Germany VL - 6187 UR - http://dx.doi.org/10.1007/978-3-642-13818-8_32 ER - TY - CONF T1 - Ontology Driven Integration of Biology Experiment Data T2 - Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters & Demos Y1 - 2009 A1 - Raghava Mutharaju A1 - Satya S. Sahoo A1 - D. Brent Weatherly A1 - Pramod Anantharam A1 - Flora Logan A1 - Amit Sheth A1 - Rick Tarleton JF - Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters & Demos CY - Cleveland, OH, USA ER - TY - CONF T1 - Ontology-Driven Provenance Management in eScience: An Application in Parasite Research T2 - On the Move to Meaningful Internet Systems: OTM 2009, Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009, Proceedings, Part II Y1 - 2009 A1 - Satya S. Sahoo A1 - D. Brent Weatherly A1 - Raghava Mutharaju A1 - Pramod Anantharam A1 - Amit Sheth A1 - Rick Tarleton ED - Robert Meersman ED - Tharam S. Dillon ED - Pilar Herrero AB -

Provenance, 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.

JF - On the Move to Meaningful Internet Systems: OTM 2009, Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009, Proceedings, Part II PB - Springer CY - Vilamoura, Portugal VL - 5871 UR - http://dx.doi.org/10.1007/978-3-642-05151-7_18 ER - TY - CONF T1 - Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences T2 - Web Information Systems Engineering - WISE 2009, 10th International Conference Y1 - 2009 A1 - Meenakshi Nagarajan A1 - Karthik Gomadam A1 - Amit Sheth A1 - Ajith Ranabahu A1 - Raghava Mutharaju A1 - Ashutosh Jadhav ED - Gottfried Vossen ED - Darrell D. E. Long ED - Jeffrey Xu Yu AB -

We 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.

JF - Web Information Systems Engineering - WISE 2009, 10th International Conference PB - Springer CY - Poznan, Poland VL - 5802 UR - http://dx.doi.org/10.1007/978-3-642-04409-0_52 ER - TY - CONF T1 - Trykipedia: Collaborative Bio-Ontology Development using Wiki Environment T2 - Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters & Demos Y1 - 2009 A1 - Pramod Anantharam A1 - Satya S. Sahoo A1 - D. Brent Weatherly A1 - Flora Logan A1 - Raghava Mutharaju A1 - Amit Sheth A1 - Rick Tarleton JF - Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters & Demos CY - Cleveland, OH, USA ER - TY - CONF T1 - Twitris: Socially Influenced Browsing T2 - Semantic Web Challenge at the 8th International Semantic Web Conference (ISWC 2009) Y1 - 2009 A1 - Ashutosh Jadhav A1 - Wenbo Wang A1 - Raghava Mutharaju A1 - Pramod Anantharam A1 - Vinh Nguyen A1 - Amit Sheth A1 - Karthik Gomadam A1 - Meenakshi Nagarajan A1 - Ajith Ranabahu AB -

In 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.

JF - Semantic Web Challenge at the 8th International Semantic Web Conference (ISWC 2009) CY - Washington DC, USA ER -