OnPerDis: Ontology-based personal name disambiguation on the Web

With the growth of web documents, the ambiguity of personal name becomes more common and brings poor performance of web search. Identifying a correct personal entity from the a piece of or the whole document is still a very challenging problem, especially for Chinese websites. In this paper, we propose a novel Ontology-based approach for Personal Name Disambiguation (named "OnPerDis"). This approach has two main steps: first, we construct person ontology (PO) with rich conceptual modeling as well as a large set of supporting instances; second, for a given personal name on the web, we create a temporary instance and extract features from the web documents, calculate the similarity between this temporary instance and the instances in the PO. The one with the highest similarity score is chosen as the appropriate personal name. Our extensive evaluations with two rich real-life datasets (CIPS-SIGHAN 2012 NERD and Chinese web documents) shows OnPerDis' efficacy on personal name disambiguation on the Web.