Extending Datalog+/1 with Probabilistic Uncertainty
Gerardo Simari
Info
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Date |
28th February 2012 (week , Hilary Term 2012) |
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Time |
11:30 |
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Place |
147 |
Abstract
The Datalog+/– family of ontology languages is especially useful for representing and reasoning
over
lightweight ontologies, and is set to play a central role in the context of query answering and
information
extraction for the Semantic Web. Recently, it has become apparent that it is necessary
to develop a principled
way to handle uncertainty in this domain. In addition to uncertainty as an
inherent aspect of the Web, one must
also deal with forms of uncertainty due to inconsistency
and incompleteness, uncertainty resulting from automatically
processing Web data, as well as
uncertainty stemming from the integration of multiple heterogeneous data sources.
In this talk, we
describe a probabilistic extension of Datalog+/- that uses Markov logic networks
as the underlying
probabilistic semantics. We especially focus on scalable algorithms for answering threshold
queries,
which correspond to the question "what is the set of all atoms that are inferred from a given probabilistic
ontology with a probability of at least p?". These queries are especially relevant to Web information
extraction,
since uncertain rules lead to uncertain facts, and only information with a certain minimum
confidence is desired.
We conclude by discussing some further directions in which this model is
being developed.
Further info
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Related series |
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