Instance Matching in Knowledge Graphs through random walks and semantics

Abstract

Instance Matching (IM) is the process of matching instances that refer to the same real-world object (e.g., the same person) across different independent Knowledge Bases (KBs). This process is considered as a key step, for instance, in the integration of different KBs. In this paper, we focus on the problem of IM across different KBs represented as Knowledge Graphs (KGs). We propose SBIGMat, a novel approach for the IM problem based on Markov random walks (RW). Our approach leverages both the local and global information mutually calculated from a pairwise similarity graph. Precisely, we first build an expanded association graph consisting of pairs of IM candidates. Then, we rank each candidate pair through the stationary distribution computed from the RW on the association graph. We propose semantic and bipartite graph-based post-processing strategies that operate on the obtained random walk ranks to optimize the final assignment of co-referents. We provide a scalable distributed implementation of our approach on top of the Spark framework and we evaluate it on benchmark datasets from the instance track of the Ontology Alignment Evaluation Initiative (OAEI). The experiments show the efficiency and scalability of SBIGMat compared to several state-of-the-art IM approaches.