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 <channel rdf:about="http://ebiquity.umbc.edu//tag/html/ipfp/?t=ipfp">
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/264/An-Efficient-Method-for-Probabilistic-Knowledge-Integration"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/129/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/67/BayesOWL"/>
      <rdf:li resource="http://ebiquity.umbc.edu/getnews/html/id/32/Zhongli-Ding-defends-dissertation"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/59/Bayes-OWL"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/936/Bayesian-Network-Revision-with-Probabilistic-Constraints"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/528/Integrating-Probability-Constraints-into-Bayesian-Nets"/>
      <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/378/Belief-Update-in-Bayesian-Networks-Using-Uncertain-Evidence"/>
      <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/236/Modifying-Bayesian-Networks-by-Probability-Constraints"/>
      <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"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/162/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/264/An-Efficient-Method-for-Probabilistic-Knowledge-Integration">
  <title><![CDATA[An Efficient Method for Probabilistic Knowledge Integration]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/264/An-Efficient-Method-for-Probabilistic-Knowledge-Integration</link>
  <description><![CDATA[Probabilistic information can come from many different sources and tends to 
involve a  part  of the domain. How can we integrate the different information about probabilities, especially when they may be inconsistent?

   There are several methods dealing with this problem, such as the well
known iterative proportional fitting procedure (IPFP),
proposed by R. Kruithof in 1937 for situations that are consistent,  and the GEMA algorithm (Generalized Expectation Maximization Algorithm) giv...]]></description>
  <dc:date>2008-10-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/129/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web">
  <title><![CDATA[BayesOWL: A Probabilistic Framework  for Uncertainty in Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/129/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web</link>
  <description><![CDATA[Ph.D. Dissertation Defense
To address the difficult but important problem of modeling uncertainty in semantic web, this research has taken a probabilistic approach and developed 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:

 a representation for encoding the probability distributions as OWL classes;
 a...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/67/BayesOWL">
  <title><![CDATA[BayesOWL]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/67/BayesOWL</link>
  <description><![CDATA[Dealing with uncertainty is crucial in ontology engineering tasks such as domain modeling, ontology reasoning, and concept mapping between ontologies. Our on-going research on modeling uncertainty in ontologies is 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 translation rules, and 3) con...]]></description>
  <dc:date>2004-11-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/getnews/html/id/32/Zhongli-Ding-defends-dissertation">
  <title><![CDATA[Zhongli Ding defends dissertation]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/32/Zhongli-Ding-defends-dissertation</link>
  <description><![CDATA[Zhongli Ding successfully defended her Ph.D. dissertation
entitled "BayesOWL: A Probabilistic Framework for Uncertainty in
Semantic Web" on December 5, 2005.  Dr. Ding came to UMBC in the Fall
of 1999 after receiving her undergraduate degree from the University
of Science and Technology of China in Hefei.  She joined the ebquity
lab in 2000 and has worked closely with Professor Yun Peng, who was
her mentor and dissertation supervisor.  She received a Masters degree in
Computer Scie...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/59/Bayes-OWL">
  <title><![CDATA[Bayes OWL]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/59/Bayes-OWL</link>
  <description><![CDATA[Dealing with uncertainty is crucial in ontology engineering tasks such
as domain modeling, ontology reasoning, and concept mapping between
ontologies. The Bayes OWL project addresses this problem by exploring
how uncertainty can be modeled in ontologies using Bayesian networks
(BN). Our approach involves extending OWL to allow additional
probabilistic markups for attaching probability information.  Having
done so, we can directly convert a probabilistically annotated OWL
ontology into ...]]></description>
  <dc:date>2003-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/936/Bayesian-Network-Revision-with-Probabilistic-Constraints">
  <title><![CDATA[Bayesian Network Revision with Probabilistic Constraints]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/936/Bayesian-Network-Revision-with-Probabilistic-Constraints</link>
  <description><![CDATA[This paper deals with an important probabilistic knowledge integration problem: revising a Bayesian network (BN) to satisfy a set of probability constraints representing new or more specific knowledge. We propose to solve this problem by adopting IPFP (iterative proportional fitting procedure) to BN. The resulting algorithm E-IPFP integrates the constraints by only changing the conditional probability tables (CPT) of the given BN while preserving the network structure; and the probability dis...]]></description>
  <dc:date>2012-03-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/528/Integrating-Probability-Constraints-into-Bayesian-Nets">
  <title><![CDATA[Integrating Probability Constraints into Bayesian Nets]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/528/Integrating-Probability-Constraints-into-Bayesian-Nets</link>
  <description><![CDATA[This paper presents a formal convergence proof for EIPFP, an algorithm that integrates low dimensional probabilistic constraints into a Bayesian network (BN) based on the mathematical procedure IPFP. It also extends E-IPFP to deal with constraints that are inconsistent with each other or with the BN structure.]]></description>
  <dc:date>2010-11-30</dc:date>
 </item>
 <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/378/Belief-Update-in-Bayesian-Networks-Using-Uncertain-Evidence">
  <title><![CDATA[Belief Update in Bayesian Networks Using Uncertain Evidence]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/378/Belief-Update-in-Bayesian-Networks-Using-Uncertain-Evidence</link>
  <description><![CDATA[This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evidence (represented as likelihood ratios) and soft evidence (represented as probability distributions). We review three existing belief update methods with uncertain evidences: virtual evidence method, Jeffrey’s rule, and IPFP (iterative proportional fitting procedure), and analyze the relations between these methods...]]></description>
  <dc:date>2006-11-13</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/236/Modifying-Bayesian-Networks-by-Probability-Constraints">
  <title><![CDATA[Modifying Bayesian Networks by Probability Constraints]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/236/Modifying-Bayesian-Networks-by-Probability-Constraints</link>
  <description><![CDATA[This paper deals with the following problem:
modify a Bayesian network to satisfy a given set
of probability constraints by only changeing its
conditional probability tables while keeping the probability
distribution of the resulting network  as
close as possible to that of the original.
We solve this problem by extending
IPFP (iterative proportional fitting procedure) to
probability distributions represented by Bayesian
networks. The resulting algorithm, E-IPFP is further
developed...]]></description>
  <dc:date>2005-07-26</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>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/162/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web">
  <title><![CDATA[BayesOWL: A Probabilistic Framework for Uncertainty in Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/162/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web</link>
  <description><![CDATA[Ph.D. Dissertation Defense
To address the difficult but important problem of modeling uncertainty in semantic web, this research has taken a probabilistic approach and developed 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:

 a representation for encoding the probability distributions as OWL classes;
 a...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-">
  <title><![CDATA[BayesOWL: A Probabilistic Framework for Uncertainty in Semantic Web (pdf)]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-</link>
  <description><![CDATA[Ph.D. Dissertation Defense
To address the difficult but important problem of modeling uncertainty in semantic web, this research has taken a probabilistic approach and developed 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:

 a representation for encoding the probability distributions as OWL classes;
 a...]]></description>
  <dc:date>2005-12-05</dc:date>
 </item>
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