Entity Search/Match in Relational Databases

Minlue Wang, Valeriia Haberland, Andrew Martin, John Howroyd, John Mark Bishop

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


We study an entity search/match problem that requires retrieved tuples match to an input entity query. We assume the input queries are of the same type as the tuples in a materialised relational table. Existing keyword search over relational databases focuses on assembling tuples from a variety of relational tables in order to respond to a keyword query. The entity queries in this work differ from the keyword queries in two ways: (i) an entity query roughly refers to an entity that contains a number of attribute values, i.e. a product entity or an address entity; (ii) there might be redundant or incorrect information in the entity queries that could lead to misinterpretations of the queries. In this paper, we propose a transformation that first converts an unstructured entity query into a multi-valued structured query, and two retrieval methods are proposed to generate a set of candidate tuples from the database. The retrieval methods essentially formulate SQL queries against the database given the multi-valued structured query. The results of a comprehensive evaluation of a large-scale database (more than 29 millions tuples) and two real-world datasets showed that our methods have a good trade-off between generating correct candidates and the retrieval time compared to baseline approaches.
Original languageEnglish
Title of host publicationProceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Place of PublicationFunchal, Madeira
Number of pages8
ISBN (Electronic)9789897582714
Publication statusPublished - Nov 2017


  • Entity search
  • Relational databases
  • Query annotation
  • Semantic search

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