Entity Search/Match in Relational Databases

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

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Paper

Entity Search/Match in Relational Databases

Topics: Business Intelligence Applications; Foundations of Knowledge Discovery in Databases; Machine Learning; Mining Text and Semi-Structured Data

In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 0IC3K, 198-205, 2017 , Funchal, Madeira, Portugal

Authors: Minlue Wang ; Valeriia Haberland ; Andrew Martin ; John Howroyd and John Mark Bishop

Affiliation: The Centre for Intelligent Data Analytics (TCIDA), Goldsmiths and University of London, United Kingdom

Keyword(s): Entity Search, Relational Databases, Query Annotation, Semantic Search.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Intelligence Applications ; Computational Intelligence ; Evolutionary Computing ; Foundations of Knowledge Discovery in Databases ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining Text and Semi-Structured Data ; Soft Computing ; Symbolic Systems

Abstract: We study an entity search/match problem that requires retrieved tuples match to the same entity as 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 a free-text entity query into a multi-valued structured query, and two retrieval methods are proposed in order to generate a set of candidate tuples from the database. The retrieval methods essentially formu late SQL queries against the database given the multi-valued structured query. The results of the 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. (More)

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Paper citation in several formats:

Wang, M.; Haberland, V.; Martin, A.; Howroyd, J. and Bishop, J. (2017). Entity Search/Match in Relational Databases. In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR; ISBN 978-989-758-271-4; ISSN 2184-3228, SciTePress, pages 198-205. DOI: 10.5220/0006498701980205

@conference{kdir17,
author={Minlue Wang. and Valeriia Haberland. and Andrew Martin. and John Howroyd. and John Mark Bishop.},
title={Entity Search/Match in Relational Databases},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR},
year={2017},
pages={198-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006498701980205},
isbn={978-989-758-271-4},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2017) - KDIR
TI - Entity Search/Match in Relational Databases
SN - 978-989-758-271-4
IS - 2184-3228
AU - Wang, M.
AU - Haberland, V.
AU - Martin, A.
AU - Howroyd, J.
AU - Bishop, J.
PY - 2017
SP - 198
EP - 205
DO - 10.5220/0006498701980205
PB - SciTePress