Semantic Guided Filtering Strategy for Best-effort Subgraph Matching in Knowledge Graphs

Abstract

Subgraph Matching is one of the fundamental problems in network analysis, with a wide range of applications ranging from drug repurposing and discovery to programming language analysis. Due to the increasing prevalence of knowledge graphs (KGs), there has been growing interests in extending existing subgraph matching algorithms to the KG domain. One of the main challenges here lies on the structural gap, which refers to the difference between the query pattern and the corresponding subgraph instance due to variations in semantic expression. To address this challenge, we propose a semantic guided subgraph matching method for knowledge graphs, which extends our prior filtering-based method. Specifically, our approach leverages an external semantic ontology to estimate the overall fitness/quality of subgraph candidates with respect to the query template. In addition, our approach incorporates effective query decomposition strategies to reduce the overall query cost. Furthermore, we develop a distributed implementation of the algorithm such that it can be scaled up to handle knowledge graphs with a large number of entities and relations. We demonstrate the effectiveness of the proposed approach on a variety of semantic networks provided in the DARPA Modeling Adversarial Activity (MAA) program.