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Path index based keywords to SPARQL query transformation for semantic data federations

Authors:

Thilini Cooray ,

University of Colombo, LK
About Thilini
School of Computing, Sri Lanka
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Gihan Wikramanayake

University of Colombo, LK
About Gihan
professor, Senior Lecturer,  School of Computing
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Abstract

Semantic web is a highly emerging research domain. Enhancing the ability of keyword query processing on Semantic Web data provides a huge support for familiarizing the usefulness of Semantic Web to the general public. Most of the existing approaches focus on just user keyword matching to RDF graphs and output the connecting elements as results. Semantic Web consists of SPARQL query language which can process queries more accurately and efficiently than general keyword matching. There are only about a couple of approaches available for transforming keyword queries to SPARQL. They basically rely on real time graph traversals? for identifying subgraphs which can connect user keywords. Those approaches are either limited to query processing on a single data store or a set of interlinked data sets. They have not focused on query processing on a federation of independent data sets which belongs to the same domain. This research proposes a Path Index based approach eliminating real time graph traversal for transforming keyword queries to SPARQL. We have introduced an ontology alignment based approach for keyword query transforming on a federation of RDF data stored using multiple heterogeneous vocabularies. Evaluation shows that the proposed approach have the ability to generate SPARQL queries which can provide highly relevant results for user keyword queries. The Path Index based query transformation approach has also achieved high efficiency compared to the existing approach.
DOI: http://doi.org/10.4038/icter.v9i1.7168
How to Cite: Cooray, T. & Wikramanayake, G., (2016). Path index based keywords to SPARQL query transformation for semantic data federations. International Journal on Advances in ICT for Emerging Regions (ICTer). 9(1), pp.12–23. DOI: http://doi.org/10.4038/icter.v9i1.7168
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Published on 13 Jul 2016.
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