Abstract

Recently, graph query is widely adopted for querying knowledge graphs. Given a query graph \(G_Q\), the graph query finds subgraphs in a knowledge graph \(G\) that exactly or approximately match \(G_Q\). We face two challenges on graph query: (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 the top-k answers effectively and efficiently. We leverage a knowledge graph embedding model to build the semantic graph \(SG_Q\), and we define the path semantic similarity (\(pss\)) over \(SG_Q\) as the metric to evaluate the answer's quality. Then, we propose an A* semantic search on \(SG_Q\) to find the top-k answers with the greatest \(pss\) via a heuristic \(pss\) estimation. Furthermore, we make an approximate optimization on A* s

Authors

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Tags

  • ANN Search

Stats

  • citations36
  • S2 citationsβ€”
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  • heat score11.76
  • arxiv keywang2019semantic

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