@inproceedings{pub2992,
    series = {Lecture Notes in Computer Science, LNCS},
    abstract = {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.},
    year = {2008},
    title = {Instance Based Clustering of Semantic Web Resources},
    booktitle = {Proceedings of the 5th European Semantic Web Conference. The Semantic Web: Research and Applications (ESWC-2008), 5th Conference, June 1-5, Tenerife, Spain},
    editor = {Sean Bechhofer and Manfred  Hauswirth and Jörg  Hoffmann and Manolis  Koubarakis},
    volume = {5021},
    pages = {303-317},
    publisher = {Springer},
    author = {Gunnar Aastrand Grimnes and Peter Edwards and Alun D.  Preece},
    keywords = {clustering instance my-own semantic}
}