An ontology-guided approach to change detection of the semantic web data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

To achieve improved availability and performance, often, local copies of remote data from autonomous sources are maintained. Web search engines are the primary examples of such services. Increasingly, these services are utilizing the Semantic Web as it is often envisioned as a machine-interpretable web. In order to keep the local repositories current, it is essential to synchronize their content with that of their original sources. Change detection is the first step to accomplish this. It is essential to have efficient change detection mechanisms as the size of the local repositories is often very large. In this paper, we present an approach that exploits the semantic relationships among the concepts in guiding the change detection process. Given changes to some seed instances, a reasoning engine fires a set of pre-defined rules to characterize the profile of the changed target instances. In addition to change detection, our proposed semantics-based approach of utilizing semantic associations can be utilized in other applications such as guiding information discovery for agents, consistency maintenance among distributed information sources, among others.

Original languageEnglish (US)
Title of host publicationJournal on Data Semantics V
PublisherSpringer Verlag
Pages130-157
Number of pages28
ISBN (Print)3540314261, 9783540314264
DOIs
StatePublished - Jan 1 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3870 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Qin, L., & Atluri, V. (2006). An ontology-guided approach to change detection of the semantic web data. In Journal on Data Semantics V (pp. 130-157). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3870 LNCS). Springer Verlag. https://doi.org/10.1007/11617808_5