Top-k star queries on knowledge graphs through semantic-aware bounding match scores

摘要

Large-scale knowledge graphs containing millions of entities are very common nowadays. Querying knowledge graphs is essential for a wide range of emerging applications, e.g., question answering and semantic search. A star query aims to identify an entity by giving a set of related entities, which is an important query type on knowledge graphs. Answering star queries can be modeled as a graph query problem. Given a query graph Q, the graph query finds subgraphs in a knowledge graph G that match Q. We face two challenges on graph query: (1) existing graph query methods usually find subgraphs that are structurally similar to Q, which cannot measure whether a subgraph match satisfies the semantics of Q (i.e., real query intention), leading to an effectiveness issue, and (2) querying a largescale knowledge graph is usually time-consuming because of the large search space. In this paper, we propose a Top -k semantic-aware graph query method over knowledge graphs for star queries, which provides semantically similar matches for Q instead of structurally similar matches. The semantic similarity of a match to Q is measured by an online computed bounding match score. By using bounds, we can efficiently prune the unpromising matches with lower semantic similarities without evaluating all matches. Extensive experiments over three real-world knowledge graphs confirm the effectiveness and efficiency of our solution. (C) 2020 Elsevier B.V. All rights reserved.