Domain Specific Graph Selection From Linked Open Data

Abstract

With the Linked Open Data (LOD) initiative, there has been a great deal of work in developing a variety of open knowledge graphs freely accessible on the Web such as DBPedia, Yago and Freebase. Lately, the focus is moving greatly towards in leveraging the knowledge graphs as a background source for various applications. Knowledge graphs can provide a valuable source of information to improve tasks such as recommendations, (semantic) similarity calculation and named entity disambiguation. One of the main usage of a knowledge graph for these applications is to find out how entities are related to each other. For example, a movie recommendation system is interested in finding out how movies are related to provide recommendation based on the implicit feedback. Existing approaches consider the neighborhood graph within predefined number of hops for this purpose. But, given a particular domain not all properties and entities might be relevant. Given the movie domain, Military Unit and Death Place of a movie director will have a significantly less importance compared to his Movies and Awards. In this work, we propose a schema driven approach to select the domain specific sub graph by taking PMI as the degree of association to rank the properties and classes of the neighbourhood graph in the context of the domain (e.g movie). We evaluate our approach in the movie domain with DBPedia knowledge graph to show the effectiveness our approach for movie recommendation.