Semantic Guided and Response Times Bounded Top-k Similarity Search over Knowledge Graphs

15 Oct 2019  ·  Yuxiang Wang, Arijit Khan, Tianxing Wu, Jiahui Jin, Haijiang Yan ·

Recently, graph query is widely adopted for querying knowledge graphs. Given a query graph $G_Q$, graph query finds subgraphs in a knowledge graph $G$ that exactly or approximately match $G_Q$. We face two challenges on graph query over a knowledge graph: (1) the structural gap between $G_Q$ and the predefined schema in $G$ causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT). In this paper, we propose a semantic guided and response-time-bounded graph query to return top-k answers effectively and efficiently. We first leverage a knowledge graph embedding model to build the semantic graph $SG_Q$ for each $G_Q$. Then we define the path semantic similarity ($pss$) over $SG_Q$ to evaluate the match's quality. We propose an A* semantic search on $SG_Q$ to find top-k answers with the greatest $pss$ via a heuristic $pss$ estimation. Furthermore, we make an approximate optimization on A* semantic search to allow users to trade off the effectiveness for SRT within a user-specific time bound. Extensive experiments over real datasets confirm the effectiveness and efficiency.

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