KBQA: Accelerate Fuzzy Path Query on Knowledge Graph

Abstract

Fuzzy path query is widely used to find the deep association of entities in many real-world applications such as knowledge graph answering and social network analysis. However, existing engines fail to support fuzzy path queries on large property graphs due to the imprecise string matching and indefinite search space. In this paper, we propose an extremely fast graph query engine KBQA, which can perform semantic matching in both entities and properties, and search arbitrarily long paths efficiently. Facing the performance problem, KBQA designs two-phase filtering strategy to accelerate candidate selection. Also, bitwise operations are adopted for fast graph exploration. Furthermore, KBQA adaptively prunes unpromising search paths based on path similarity. Extensive experiments show that KBQA outperforms all state-of-the-art graph databases by 2 × ∼ 10 × and searches all 6-hop paths within ten seconds. Our system has been applied in the ICT field and has achieved remarkable results.