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 <channel rdf:about="http://ebiquity.umbc.edu/tag/ipfp/">
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    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/421/An-Efficient-Method-for-Probabilistic-Knowledge-Integration"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/278/BayesOWL-A-Probabilistic-Framework-for-Semantic-Web"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/235/A-Bayesian-Methodology-towards-Automatic-Ontology-Mapping"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/204/A-Bayesian-Approach-to-Uncertainty-Modeling-in-OWL-Ontology"/>
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 <image rdf:about="http://ebiquity.umbc.edu/img/logo.jpg">
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/421/An-Efficient-Method-for-Probabilistic-Knowledge-Integration">
  <title><![CDATA[An Efficient Method for Probabilistic Knowledge Integration]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/421/An-Efficient-Method-for-Probabilistic-Knowledge-Integration</link>
  <description><![CDATA[This paper presents an efficient method, SMOOTH, for modifying a joint probability distribution to satisfy a set of inconsistent constraints. It extends the well-known “iterative proportional fitting procedure” (IPFP), which only works with consistent constraints. Comparing with existing methods, SMOOTH is computationally more efficient and insensitive to data. Moreover, SMOOTH can be easily integrated with Bayesian networks for Bayes reasoning with inconsistent constraints.]]></description>
  <dc:date>2008-11-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/278/BayesOWL-A-Probabilistic-Framework-for-Semantic-Web">
  <title><![CDATA[BayesOWL: A Probabilistic Framework for Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/278/BayesOWL-A-Probabilistic-Framework-for-Semantic-Web</link>
  <description><![CDATA[To address the difficult but important problem of modeling uncertainty in semantic web,
this research takes a probabilistic approach and develops a theoretical framework, named
BayesOWL, that incorporates the Bayesian network (BN), a widely used graphic model
for probabilistic interdependency, into the web ontology language OWL. This framework
consists of three key components: 1) a representation of probabilistic constraints as OWL
statements; 2) a set of structural translation rules and...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/235/A-Bayesian-Methodology-towards-Automatic-Ontology-Mapping">
  <title><![CDATA[A Bayesian Methodology towards Automatic Ontology Mapping]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/235/A-Bayesian-Methodology-towards-Automatic-Ontology-Mapping</link>
  <description><![CDATA[This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. The pro-posed method includes four components: 1) learning prob-abilities (priors about concepts, conditionals between sub-concepts and superconcepts, and raw semantic similarities between concepts in two different ontologies) using Naive Bayes text classification technique, by explicitl...]]></description>
  <dc:date>2005-07-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/204/A-Bayesian-Approach-to-Uncertainty-Modeling-in-OWL-Ontology">
  <title><![CDATA[A Bayesian Approach to Uncertainty Modeling in OWL Ontology]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/204/A-Bayesian-Approach-to-Uncertainty-Modeling-in-OWL-Ontology</link>
  <description><![CDATA[Dealing with uncertainty is crucial in ontology
engineering tasks such as domain modeling, ontology reasoning,
and concept mapping between ontologies. This paper presents our
on-going research on modeling uncertainty in ontologies based on
Bayesian networks (BN). This includes 1) extending OWL to
allow additional probabilistic markups for attaching probability
information, 2) directly converting a probabilistically annotated
OWL ontology into a BN structure by a set of structural
tran...]]></description>
  <dc:date>2004-11-15</dc:date>
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