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k-Nearest Keyword Search in RDF Graphs

18 Pages Posted: 9 Jul 2018 Publication Status: Accepted

See all articles by Xiang Lian

Xiang Lian

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science

Eugenio D. Hoyos

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science

Artem Chebotko

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science

Bin Fu

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science

Christine Reilly

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science

Abstract

Resource Description Framework (RDF) has been widely used as a W3C standard to describe the resource information in the Semantic Web. A standard SPARQL query over RDF data requires query issuers to fully understand the domain knowledge of the data. Because of this fact, SPARQL queries over RDF data are not flexible and it is difficult for nonexperts to create queries without knowing the underlying data domain. Inspired by this problem, in this paper, we propose and tackle a novel and important query type, namely k-nearest keyword (k-NK) query, over a large RDF graph. Specifically, a k-NK query obtains k closest pairs of vertices, (vi, ui), in the RDF graph, that contain two given keywords q and w, respectively, such that ui is the nearest vertex of vi that contains the keyword w. To efficiently answer k-NK queries, we design effective pruning methods for RDF graphs both with and without an ontology, which can greatly reduce the query search space. Moreover, to facilitate our pruning strategies, we propose effective indexing mechanisms on RDF graphs with/without ontologies to enable fast k-NK query answering. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed k-NK query processing approaches.

Keywords: RDF graph, nearest keyword search, Semantic Web

Suggested Citation

Lian, Xiang and Hoyos, Eugenio D. and Chebotko, Artem and Fu, Bin and Reilly, Christine, k-Nearest Keyword Search in RDF Graphs (2013). Journal of Web Semantics First Look, Available at SSRN: https://ssrn.com/abstract=3199072 or http://dx.doi.org/10.2139/ssrn.3199072

Xiang Lian (Contact Author)

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science ( email )

Engineering Building, Room 3.295
Edinburg, TX 78539
United States

Eugenio D. Hoyos

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science ( email )

Engineering Building, Room 3.295
Edinburg, TX 78539
United States

Artem Chebotko

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science ( email )

Engineering Building, Room 3.295
Edinburg, TX 78539
United States

Bin Fu

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science ( email )

Engineering Building, Room 3.295
Edinburg, TX 78539
United States

Christine Reilly

University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American) - Department of Computer Science ( email )

Engineering Building, Room 3.295
Edinburg, TX 78539
United States

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