An MCL-Based Text Mining Approach for Namesake Disambiguation on the Web

Abstract

In this paper, we propose a Markov Clustering (MCL) based text mining approach for namesake disambiguation on the Web. The novelty of the proposed technique lies in modeling the collection of web pages using a weighted graph structure and applying MCL to crystalize it into different clusters, each one containing the web pages related to a particular namesake individual. The proposed method focuses on three broad and realistic aspects to cluster web pages retrieved through search engines - content overlapping, structure overlapping, and local context overlapping. The efficacy of the proposed method is demonstrated through experimental evaluations on standard datasets.