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Publication - Dr Valeriia Haberland

    Entity Search/Match in Relational Databases


    Wang, M, Haberland, V, Martin, A, Howroyd, J & Bishop, JM, 2017, ‘Entity Search/Match in Relational Databases’. in: Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. SciTePress, Funchal, Madeira, pp. 198-205


    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.

    Full details in the University publications repository