Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

TL;DR

This paper presents a novel multi-sensor data fusion approach using Dempster-Shafer theory and Shannon's entropy to improve expert finding in academic domains, effectively integrating heterogeneous information sources.

Contribution

It introduces a new framework combining textual, citation, and profile data with evidence theory and entropy for expert ranking, outperforming traditional methods.

Findings

Effective fusion of heterogeneous data sources.

Comparable performance to supervised algorithms.

Improved accuracy in expert ranking.

Abstract

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster-Shafer theory of evidence and Shannon's entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework.