Semantic relation composition in large scale knowledge bases
Kristian Kolthoff, Arnab Dutta · MADOC (University of Mannheim) · 2015
Semantic relation composition is a generalized approach for finding conjunctive relation paths in a knowledge base (KB). In semantic web, direct and inverse relationships between entities provide us with ample of explicit knowledge. But there is a plethora of implicit knowledge beyond these direct paths. Consider a knowledge graph, we can achieve deeper insights about a particular entity if we consider the information shared by its neighboring entities via its adjacent relation paths of arbitrary lengths. In this paper, we devise a technique to automatically discover semantically enriched conjunctive relations in a KB. Our approach is generalized for any KB and requires no additional parameter tuning. Particularly, we employ classical rule mining techniques to perform relation composition on knowledge graphs to learn first order rules. We evaluate our proposed methodology on two state of the art information extraction systems, DBpedia and Yago with promising results in terms of generating high precision rules. Furthermore, we make the rules publicly available for community usage.