This article presents a name disambiguation approach to resolve ambiguities between person names and group web pages according to the individuals they refer to. The proposed approach exploits two important sources of entity-centric semantic information extracted from web pages, including personal attributes and social relationships. It takes as input the web pages that are results for a person name search. The web pages are analyzed to extract personal attributes and social relationships. The personal attributes and social relationships are mapped into an undirected weighted graph, called attribute-relationship graph. A graph-based clustering algorithm is proposed to group the nodes representing the web pages, each of which refers to a person entity. The outcome is a set of clusters such that the web pages within each cluster refer to the same person. We show the effectiveness of our approach by evaluating it on large-scale datasets WePS-1, WePS-2, and WePS-3. Experimental results are encouraging and show that the proposed method clearly outperforms several baseline methods and also its counterparts.
