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

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* 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 of our solution.