=Paper=
{{Paper
|id=Vol-1265/invited
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1265/abstractdAmato.pdf
|volume=Vol-1265
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1265/abstractdAmato.pdf</pdf>
<pre>
Machine Learning for Ontology Mining:
       Perspectives and Issues

                           Claudia d’Amato

                        University of Bari, Italy
                       claudia.damato@uniba.it



Abstract. In the Semantic Web view, ontologies play a key role. They
act as shared vocabularies to be used for semantically annotating Web
resources and they allow to perform deductive reasoning for making
explicit knowledge that is implicitly contained within them. However,
noisy/inconsistent ontological knowledge bases may occur, being the Web
a shared and distributed environment, thus making deductive reason-
ing no more straightforwardly applicable. Machine learning techniques,
and specifically inductive learning methods, could be fruitfully exploited
in this case. Additionally, machine learning methods, jointly with stan-
dard reasoning procedure, could be usefully employed for discovering
new knowledge from an ontological knowledge base, that is not logically
derivable. The focus of the talk will be on various ontology mining prob-
lems and on how machine learning methods could be exploited for coping
with them. For ontology mining are meant all those activities that allow
to discover hidden knowledge from ontological knowledge bases, by pos-
sibly using only a sample of data. Specifically, by exploiting the volume
of the information within an ontology, machine learning methods could
be of great help for (semi-)automatically enriching and refining exist-
ing ontologies, for detecting concept drift and novelties within ontologies
and for discovering hidden knowledge patterns (also possibly exploiting
other sources of information). If on one hand this means to abandon
sound and complete reasoning procedures for the advantage of uncertain
conclusions, on the other hand this could allow to reason on large scale
and to to dial with the intrinsic uncertainty characterizing the Web, that,
for its nature, could have incomplete and/or contradictory information.

</pre>