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

  • Authors:

  • Boanerges Aleman-Meza;Meenakshi Nagarajan;Li Ding;Amit Sheth;I. Budak Arpinar;Anupam Joshi;Tim Finin

  • Affiliations:

  • University of Georgia, GA;Wright State University, OH;Stanford University, CA;Wright State University, OH;University of Georgia, GA;University of Maryland, Baltimore County, MD;University of Maryland, Baltimore County, MD

  • Venue:
  • ACM Transactions on the Web (TWEB)
  • Year:
  • 2008

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Abstract

In this article, we demonstrate the applicability of semantic techniques for detection of Conflict 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 work where we integrated the collaborative network of a subset of DBLP researchers with persons in a Friend-of-a-Friend social network (FOAF). Our method finds the connections between people, measures collaboration strength, and includes heuristics that use friendship/affiliation information to provide an estimate of potential COI in a peer-review scenario. Evaluations are presented by measuring what could have been the COI between accepted papers in various conference tracks and their respective program committee members. The experimental results demonstrate that scalability can be achieved by using a dataset of over 3 million entities (all bibliographic data from DBLP and a large collection of FOAF documents).