An optimal model for domain specific named entity linking with heterogeneous information networks

Abstract

In this web era, entity linking plays a major role. In the web the information’s are associated with different kinds of data and objects. Heterogeneous information networks (HIN) involved multi composed interlinked interconnected objects with various types of connections which is more prominent in this real world. Most of the research work focused towards processing homogeneous networks as well as linking entities with Wikipedia as knowledge base. In this paper we proposed a probabilistic based domain specific entity linking system that will link named entity mentions detected from unstructured web text corpus with corresponding entity in the existing domain specific Heterogeneous information networks as knowledge base. This work is most challenging due to entity name ambiguity as well as knowledge in the network that are limited one. The proposed model framework presents a model that will link named entity from unstructured web text with domain specific Heterogeneous information network mainly focuses on to learn the weight of meta path. The experiments are done over real world dataset such as DBLP and IMDB dataset.