Personal named entity linking based on simple partial tree matching and context free grammar

Abstract

Personal name disambiguation is the task of linking a personal name to a unique comparable entry in the real world, also known as named entity linking (NEL). Algorithms for NEL consist of three main components: extractor, searcher, and disambiguator. Existing approaches for NEL use exact-matched look-up over the surface form to generate a set of candidate entities in each of the mentioned names. The exact-matched look-up is wholly inadequate to generate a candidate entity due to the fact that the personal names within a web page lack uniform representation. In addition, the performance of a disambiguator in ranking candidate entities is limited by context similarity. Context similarity is an inflexible feature for personal disambiguation because natural language is highly variable. We propose a new approach that can be used to both identify and disambiguate personal names mentioned on a web page. Our NEL algorithm uses: as an extractor: a control flow graph; AlchemyAPI, as a searcher: Personal Name Transformation Modules (PNTM) based on Context Free Grammar and the Jaro-Winkler text similarity metric and as a disambiguator: the entity coherence method: the Occupation Architecture for Personal Name Disambiguation (OAPnDis), personal name concepts and Simple Partial Tree Matching (SPTM). Experimental results, evaluated on real-world data sets, show that the accuracy of our NEL is 92%, which is higher than the accuracy of previously used methods.