Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings
WWW '20: The Web Conference 2020 Taipei Taiwan April, 2020(2020)
摘要
Keyword search is a prominent approach to querying Web data. For graph-structured data, a widely accepted semantics for keywords is based on group Steiner trees. For this NP-hard problem, existing algorithms with provable quality guarantees have prohibitive run time on large graphs. In this paper, we propose practical approximation algorithms with a guaranteed quality of computed answers and very low run time. Our algorithms rely on Hub Labeling (HL), a structure that labels each vertex in a graph with a list of vertices reachable from it, which we use to compute distances and shortest paths. We devise two HLs: a conventional static HL that uses a new heuristic to improve pruned landmark labeling, and a novel dynamic HL that inverts and aggregates query-relevant static labels to more efficiently process vertex sets. Our approach allows to compute a reasonably good approximation of answers to keyword queries in milliseconds on million-scale knowledge graphs.
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关键词
knowledge graph, keyword search, group Steiner tree, hub labeling
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