Scalable semantic analytics on social networks for addressing the problem of conflict of interest detection

Scalable semantic analytics on social networks for addressing the problem of conflict of interest... Scalable Semantic Analytics on Social Networks for Addressing the Problem of Con ‚ict of Interest Detection BOANERGES ALEMAN-MEZA University of Georgia MEENAKSHI NAGARAJAN Wright State University LI DING Stanford University AMIT SHETH Wright State University I. BUDAK ARPINAR University of Georgia and ANUPAM JOSHI and TIM FININ University of Maryland, Baltimore County In this article, we demonstrate the applicability of semantic techniques for detection of Con ‚ict of Interest (COI). We explain the common challenges involved in building scalable Semantic Web applications, in particular those addressing connecting-the-dots problems. We describe in detail the challenges involved in two important aspects on building Semantic Web applications, namely, data acquisition and entity disambiguation (or reference reconciliation). We extend upon our previous This research was supported by NSF-ITR Awards #IIS-0325464 and #0714441 titled ˜SemDIS: Discovering Complex Relationships in the Semantic Web. ™ This article is an extended version of our authors ™ paper œSemantic Analysis on Social Networks: Experiences in Addressing the Problem of Con ‚ict of Interest Detection,  which appears in the Proceedings of the International World Wide Web Conference (WWW ™06). Authors ™ addresses: B. Aleman-Meza and I. B. Arpinar, LSDIS Lab, Department of Computer Science, University of Georgia, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on the Web (TWEB) Association for Computing Machinery

Scalable semantic analytics on social networks for addressing the problem of conflict of interest detection

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2008 by ACM Inc.
ISSN
1559-1131
DOI
10.1145/1326561.1326568
Publisher site
See Article on Publisher Site

Abstract

Scalable Semantic Analytics on Social Networks for Addressing the Problem of Con ‚ict of Interest Detection BOANERGES ALEMAN-MEZA University of Georgia MEENAKSHI NAGARAJAN Wright State University LI DING Stanford University AMIT SHETH Wright State University I. BUDAK ARPINAR University of Georgia and ANUPAM JOSHI and TIM FININ University of Maryland, Baltimore County In this article, we demonstrate the applicability of semantic techniques for detection of Con ‚ict of Interest (COI). We explain the common challenges involved in building scalable Semantic Web applications, in particular those addressing connecting-the-dots problems. We describe in detail the challenges involved in two important aspects on building Semantic Web applications, namely, data acquisition and entity disambiguation (or reference reconciliation). We extend upon our previous This research was supported by NSF-ITR Awards #IIS-0325464 and #0714441 titled ˜SemDIS: Discovering Complex Relationships in the Semantic Web. ™ This article is an extended version of our authors ™ paper œSemantic Analysis on Social Networks: Experiences in Addressing the Problem of Con ‚ict of Interest Detection,  which appears in the Proceedings of the International World Wide Web Conference (WWW ™06). Authors ™ addresses: B. Aleman-Meza and I. B. Arpinar, LSDIS Lab, Department of Computer Science, University of Georgia,

Journal

ACM Transactions on the Web (TWEB)Association for Computing Machinery

Published: Feb 1, 2008

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