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

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

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ACM Transactions on the Web (TWEB) archive
Volume 2 , Issue 1 (February 2008) table of contents

Article No. 7

Year of Publication: 2008

ISSN:1559-1131

Authors

Boanerges Aleman-Meza	University of Georgia, GA
Meenakshi Nagarajan	Wright State University, OH
Li Ding	Stanford University, CA
Amit Sheth	Wright State University, OH
I. Budak Arpinar	University of Georgia, GA
Anupam Joshi	University of Maryland, Baltimore County, MD
Tim Finin	University of Maryland, Baltimore County, MD

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ACM New York, NY, USA

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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).

REFERENCES

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