Instance Based Clustering of Semantic Web Resources

Gunnar Aastrand Grimnes, Pete Edwards, Alun David Preece

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

38 Citations (Scopus)


The original Semantic Web vision was explicit in the need for intelligent autonomous agents that would represent users and help them navigate the Semantic Web. We argue that an essential feature for such agents is the capability to analyse data and learn. In this paper we outline the challenges and issues surrounding the application of clustering algorithms to Semantic Web data. We present several ways to extract instances from a large RDF graph and computing the distance between these. We evaluate our approaches on three different data-sets, one representing a typical relational database to RDF conversion, one based on data from a ontologically rich Semantic Web enabled application, and one consisting of a crawl of FOAF documents; applying both supervised and unsupervised evaluation metrics. Our evaluation did not support choosing a single combination of instance extraction method and similarity metric as superior in all cases, and as expected the behaviour depends greatly on the data being clustered. Instead, we attempt to identify characteristics of data that make particular methods more suitable.
Original languageEnglish
Title of host publicationThe Semantic Web: Research and Applications
Subtitle of host publicationProceedings of Fifth European Semantic Web Conference (ESWC 2008)
EditorsSean Bechhofer, Manfred Hauswirth, Jorg Hoffmann, Manolis Koubarakis
Place of PublicationHeidelberg, Germany
Number of pages15
ISBN (Electronic)978-3-540-68234-9
ISBN (Print)978-3-540-68233-2
Publication statusPublished - 24 May 2008
Event5th European Semantic Web Conference, ESWC 2008 - Tenerife, Spain
Duration: 1 Jun 20085 Jun 2008

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference5th European Semantic Web Conference, ESWC 2008


  • semantic web
  • data-mining
  • clustering


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