=Paper=
{{Paper
|id=Vol-1433/lamma
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1433/lamma.pdf
|volume=Vol-1433
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1433/lamma.pdf</pdf>
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Technical Communications of ICLP 2015. Copyright with the Authors.                        1




              (Probabilistic) Description Logics
                                     Evelina Lamma
                                University of Ferrara, Italy
                             (e-mail: evelina.lamma@unife.it)




                                        Abstract
The Semantic Web aims at making information available in a form that is understandable
and automatically manageable by machines. Ontologies are engineering artifacts used to
this purpose, and Description Logics (DLs) are a family of logic-based languages par-
ticularly suitable for modeling (and reasoning upon) ontologies and the Semantic Web.
Great effort has been spent in identifying decidable or even tractable DLs. Efficient DLs
reasoners have been implemented in procedural, object-oriented languages or Prolog.
   Nonetheless, incompleteness or uncertainty is intrinsic of much information on the World
Wide Web. This motivated the research in Probabilistic DLs, some of which derived from
approaches from the Logic Programming area. Conversely, for knowledge representation
and reasoning, integration with rules and rule-based reasoning is also crucial in the so-
called Semantic Web stack vision.
   In this talk, I will focus on probabilistic DLs. First, I will briefly overview DLs and
reasoning systems. After recalling the Distribution Semantics from Probabilistic Logic
Programming, I will show how it and other probabilistic approaches have been applied to
DLs, and what inference systems for Probabilistic DLs are available. Learning Probabilistic
DL theories is also an interesting issue. A demo of a Web-based system for Probabilistic
DLs implemented in SWI Prolog, and SWISH, will conclude this part of the talk.
   A further research activity has been conducted by the AI and LP community in order to
facilitate the integration of DL theories with rules and rule-based reasoning, since this is
also crucial in the Semantic Web. Proposals like Datalog+/- and its extensions, ASP-based
systems or Abductive Logic Programming for modeling and reasoning upon ontologies,
are significant attempts which should be considered seriously by the LP community. I will
briefly mention these approaches, ending the talk.

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