The Fundamentals of iSPARQL: A Virtual Triple Approach for Similarity-Based Semantic Web Tasks

Abstract

Abstract. This research explores three SPARQL-based techniques to solve Semantic Web tasks that often require similarity measures, such as semantic data integration, ontology mapping, and Semantic Web service matchmaking. Our aim is to see how far it is possible to integrate customized similarity functions (CSF) into SPARQL to achieve good results for these tasks. Our first approach exploits virtual triples calling property functions to establish virtual relations among resources under comparison; the second approach uses extension functions to filter out resources that do not meet the requested similarity criteria; finally, our third technique applies new solution modifiers to post-process a SPARQL solution sequence. The semantics of the three approaches are formally elaborated and discussed. We close the paper with a demonstration of the usefulness of our iSPARQL framework in the context of a data integration and an ontology mapping experiment.

Improving semantic web services discovery using SPARQL-based repository filtering

“…Furthermore, different authors propose extensions to further improve reasoning features [17], expressiveness of queries [18], or even approaches that add DL-based languages features [13]. However, in this work we stick to SPARQL 1.0 (the recommendation at the time of writing) to improve discovery processes, though some of the extensions discussed can be also applied (see Section 6).…”

“…In this proposal, authors use a SPARQL extension (iSPARQL [17]) that enables the introduction of similarity operators into query elements. Thus, different similarity strategies are combined with logic-based discovery in order to improve precision and recall of the matchmaking process.…”

SEON: a pyramid of ontologies for software evolution and its applications

“…The SPARQL extension presented by Kiefer et al, for example, adds machine learning algorithms (SPARQL-ML [38]) and similarity joins (iSPARQL [36]) to the Semantic Web. Both extensions could lead to a complete new family of Sofas services or at least simplify the implementation of existing ones.…”

Towards software analysis as a service

“…In this way it is possible to reach some semantic interoperability between a large number of ontologies accessible "under" some upper ontology; (2) with OWL Description Logic foundation it is possible to perform automatic reasoning and derive additional knowledge; (3) we can use a powerful query language such as SPARQL or its extension iSPARQL [21], that uses similarity operators to query for similar entities; and (4) in contrast to XML and XQuery [3] that operate on the structure of the data, OWL treats data based on its semantics. This allows for an extension of the data model with no backwards compatibility problems with existing tools.…”