Publikation
Instance Based Clustering of Semantic Web Resources
Gunnar Aastrand Grimnes; Peter Edwards; Alun D. Preece
In: Sean Bechhofer; Manfred Hauswirth; Jörg Hoffmann; Manolis Koubarakis (Hrsg.). Proceedings of the 5th European Semantic Web Conference. European Semantic Web Conference (ESWC-2008), 5th, June 1-5, Tenerife, Spain, Pages 303-317, Lecture Notes in Computer Science (LNCS), Vol. 5021, Springer, 2008.
Zusammenfassung
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.