Semantic Analytics on Social Networks: Experiences in Addressing the Problem of Conflict of Interest Detection

• Boanerges Aleman-Meza, LSDIS Lab, Dept. of Computer Science. University of Georgia, USA
• Meenakshi Nagarajan, LSDIS Lab, Dept. of Computer Science. University of Georgia, USA
• Cartic Ramakrishnan, LSDIS Lab, Dept. of Computer Science. University of Georgia, USA
• Li Ding, University of Maryland Baltimore County, USA
• Pranam Kolari, Department of Computer Science and Electrical Engineering University of Maryland, USA
• Amit Sheth, Semagix, -
• I. Budak Arpinar, LSDIS Lab, Dept. of Computer Science. University of Georgia, USA
• Anupam Joshi, Department of Computer Science and Electrical Engineering University of Maryland, USA
• Tim Finin, Department of Computer Science and Electrical Engineering University of Maryland, USA

Full text:

Track: Semantic Web

Slot: 16:00-17:30, Thursday 25th May

In this paper, we describe a Semantic Web application that detects Conflict of Interest (COI) relationships among potential reviewers and authors of scientific papers. This application discovers various semantic associations' between the reviewers and authors in a populated ontology to determine a degree of Conflict of Interest. This ontology was created by integrating entities and relationships from two social networks, namely "knows," from a FOAF (Friend-of-a-Friend) social network and "co-author," from the underlying co-authorship network of the DBLP bibliography. We describe our experiences developing this application in the context of a class of Semantic Web applications, which have important research and engineering challenges in common. In addition, we present an evaluation of our approach for real-life COI detection.

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