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Scalable aggregate keyword query over knowledge graph
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DOI：10.1016/j.future.2020.02.011.png)
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Abstract
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Existing keyword query systems over knowledge graphs are easy to use and can produce interesting results. However, they cannot address even simple aggregate queries (i.e., a query that needs statistics such as COUNT, SUM, AVG, MAX, MIN, >, < and =), and the sizes of existing schema graphs grow exponentially with the growth of the number of types or predicates in the knowledge graph, so that they have low scalability for building SPARQL statements. Therefore, we propose a framework called SAKQ (scalable aggregate keyword query over knowledge graph) that enables users to pose aggregate queries using simple keywords. First, we propose a scalable schema graph (i.e., type-predicate graph) that consists of the relationships between types and predicates, which has a small data size and contains all information needed for building SPARQL statements. Second, based on the type-predicate graph, we propose two algorithms to build query graphs with aggregation and transform the query graphs into SPARQL statements with aggregation. Finally, the experimental results over the benchmark datasets demonstrate that SAKQ can answer various general aggregate keyword queries. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Knowledge graph
Question answering
Keyword search
Aggregation
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