Keyword search over schema-less RDF datasets by SPARQL query compilation. Issue 102 (December 2021)

• Record Type: Journal Article
• Authors: Izquierdo, Yenier T.; García, Grettel M.; Menendez, Elisa; Leme, Luiz André P.P.; Neves, Angelo; Lemos, Melissa; Finamore, Anna Carolina; Oliveira, Carlos; Casanova, Marco A.
• Is Part Of: Information systems. Issue 102(2021)
• Publication Date: 2021-12
• Subjects: RDF keyword search -- SPARQL -- RDF -- KMV-synopses; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2021.101814
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: This article introduces an algorithm to automatically translate a user-specified keyword-based query K to a SPARQL query Q so that the answers Q returns are also answers for K . The algorithm does not rely on an RDF schema, but it synthesizes SPARQL queries by exploring the similarity between the property domains and ranges, and the class instance sets observed in the RDF dataset. It estimates set similarity based on set synopses, which can be efficiently pre-computed in a single pass over the RDF dataset. The article includes two sets of experiments with an implementation of the algorithm. The first set of experiments shows that the implementation outperforms a baseline RDF keyword search tool that explores the RDF schema, while the second set of experiments indicate that the implementation performs better than the state-of-the-art TSA+BM25 and TSA+VDP keyword search systems over RDF datasets based on the "virtual documents" approach. Highlights: An algorithm to automatically translate keyword-based queries to SPARQL queries is introduced. The algorithm does not rely on RDF schemas. The algorithm outperforms a baseline schema-based RDF keyword search tool. The algorithm outperforms two "virtual documents" RDF keyword search tools. A measure named Graph Relevance Ratio (GRR) is proposed.