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A Rule-Based Transducer for Querying Incompletely Aligned Datasets

Published:27 September 2018Publication History
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Abstract

A growing number of Linked Open Data sources (from diverse provenances and about different domains) that can be freely browsed and searched to find and extract useful information have been made available. However, access to them is difficult for different reasons. This study addresses access issues concerning heterogeneity. It is common for datasets to describe the same or overlapping domains while using different vocabularies. Our study presents a transducer that transforms a SPARQL query suitably expressed in terms of the vocabularies used in a source dataset into another SPARQL query suitably expressed for a target dataset involving different vocabularies. The transformation is based on existing alignments between terms in different datasets. Whenever the transducer is unable to produce a semantically equivalent query because of the scarcity of term alignments, the transducer produces a semantic approximation of the query to avoid returning the empty answer to the user. Transformation across datasets is achieved through the management of a wide range of transformation rules. The feasibility of our proposal has been validated with a prototype implementation that processes queries that appear in well-known benchmarks and SPARQL endpoint logs. Results of the experiments show that the system is quite effective in achieving adequate transformations.

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            • Published in

              cover image ACM Transactions on the Web
              ACM Transactions on the Web  Volume 12, Issue 4
              November 2018
              215 pages
              ISSN:1559-1131
              EISSN:1559-114X
              DOI:10.1145/3281744
              Issue’s Table of Contents

              Copyright © 2018 ACM

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 27 September 2018
              • Revised: 1 May 2018
              • Accepted: 1 May 2018
              • Received: 1 July 2015
              Published in tweb Volume 12, Issue 4

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