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Science and Technology Studies Commons™
Open Access. Powered by Scholars. Published by Universities.®
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Articles 61 - 66 of 66
Full-Text Articles in Science and Technology Studies
Strategic Importance Of Higher Education And Research In Positioning Gujarat For Global Competitiveness, Amit P. Sheth
Strategic Importance Of Higher Education And Research In Positioning Gujarat For Global Competitiveness, Amit P. Sheth
Kno.e.sis Publications
No abstract provided.
Traveling The Semantic Web Through Space, Theme And Time, Amit P. Sheth, Matthew Perry
Traveling The Semantic Web Through Space, Theme And Time, Amit P. Sheth, Matthew Perry
Kno.e.sis Publications
In this installment of Semantics and Services, we further develop the idea of spatial, temporal, and thematic (STT) processing of semantic Web data and describe the Web infrastructure needed to support it. Starting from Ramesh Jain's vision of the EventWeb as a view of what's possible with a Web that better accommodates all three dimensions of event-related information (thematic, spatial, and temporal), we outline the architecture needed to support it and current research that aims to realize it.
Approximate Owl-Reasoning With Screech, Tuvshintur Tserendorj, Sebastian Rudolph, Markus Krotzsch, Pascal Hitzler
Approximate Owl-Reasoning With Screech, Tuvshintur Tserendorj, Sebastian Rudolph, Markus Krotzsch, Pascal Hitzler
Computer Science and Engineering Faculty Publications
Applications of expressive ontology reasoning for the Semantic Web require scalable algorithms for deducing implicit knowledge from explicitly given knowledge bases. Besides the development of more effi- cient such algorithms, awareness is rising that approximate reasoning solutions will be helpful and needed for certain application domains. In this paper, we present a comprehensive overview of the Screech approach to approximate reasoning with OWL ontologies, which is based on the KAON2 algorithms, facilitating a compilation of OWL DL TBoxes into Datalog, which is tractable in terms of data complexity. We present three different instantiations of the Screech approach, and report on …
Collaborative Ro1 With Ncbo Semantics And Services Enabled Problem Solving Environment For Trypanosoma Cruzi, Amit P. Sheth, Rick Tarleton, Prashant Doshi, Mark Musen, Natasha Noy, Satya S. Sahoo, Daniel B. Weatherly
Collaborative Ro1 With Ncbo Semantics And Services Enabled Problem Solving Environment For Trypanosoma Cruzi, Amit P. Sheth, Rick Tarleton, Prashant Doshi, Mark Musen, Natasha Noy, Satya S. Sahoo, Daniel B. Weatherly
Kno.e.sis Publications
No abstract provided.
Monetizing User Activity On Social Networks, Meenakshi Nagarajan, Kamal Baid, Amit P. Sheth, Shaojun Wang
Monetizing User Activity On Social Networks, Meenakshi Nagarajan, Kamal Baid, Amit P. Sheth, Shaojun Wang
Kno.e.sis Publications
In this work, we investigate techniques to monitize user activity on public forums, marketplaces and groups on social network sites. Our approach involves (a) identifying the monetization potential of user posts and (b) eliminating o- topic content in monetizable posts to use the most relevant keywords for advertising. Our first user study involving 30 users and data from MySpace and Facebook, shows that 52% of ad impressions shown after using our system were more targeted compared to the 30% relevant impressions generated without using our system. A second smaller study suggests that profile ads that are based on user activity …
Joint Extraction Of Compound Entities And Relationships From Biomedical Literature, Cartic Ramakrishnan, Pablo N. Mendes, Rodrigo A.T.S. De Gama, Guilherme C.N. Ferreira, Amit P. Sheth
Joint Extraction Of Compound Entities And Relationships From Biomedical Literature, Cartic Ramakrishnan, Pablo N. Mendes, Rodrigo A.T.S. De Gama, Guilherme C.N. Ferreira, Amit P. Sheth
Kno.e.sis Publications
In this paper we identify some limitations of contemporary information extraction mechanisms in the context of biomedical literature. We present an extraction mechanism that generates structured representations of textual content. Our extraction mechanism achieves this by extracting compound entities, and relationships between them, occuring in text. A detailed evaluation of the relationship and compound entities extracted is presented. Our results show over 62% average precision across 8 relationship types tested with over 82% average precision for compound entity identification1.