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 <channel rdf:about="http://ebiquity.umbc.edu//tag/html/knowledge integration/?t=knowledge+integration">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tag/html/knowledge integration/?t=knowledge+integration]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for knowledge integration]]></description>
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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/207/Using-the-Semantic-Web-to-support-knowledge-integration-retrieval-and-expansion-for-ecoinformatics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/121/OWL-leaves-the-nest"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/913/On-the-Integration-of-Inconsistent-Knowledge-with-Bayseian-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/931/Inconsistent-Knowledge-Integration-with-Bayesian-Network"/>
      <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/421/An-Efficient-Method-for-Probabilistic-Knowledge-Integration"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/157/OWL-Leaves-the-Nest-Knowledge-Integration-for-Ubiquitious-Agents"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/158/OWL-Leaves-the-Nest-Knowledge-Integration-for-Ubiquitious-Agents"/>
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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/207/Using-the-Semantic-Web-to-support-knowledge-integration-retrieval-and-expansion-for-ecoinformatics">
  <title><![CDATA[Using the Semantic Web to support knowledge integration, retrieval and expansion for ecoinformatics]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/207/Using-the-Semantic-Web-to-support-knowledge-integration-retrieval-and-expansion-for-ecoinformatics</link>
  <description><![CDATA[Today, the information on the World Wide Web is growing at
an astonishing rate providing a rapidly expanding source of
valuable data. The abundance of distributed information on
the Web increases the importance of efficient organization,
sharing and retrieval of available data. This is
particularly important in scientific research where
efficient collaboration, exchange of results of experiments
and observations, fast discovery of relevant information and
data integration from differe...]]></description>
  <dc:date>2007-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/121/OWL-leaves-the-nest">
  <title><![CDATA[OWL leaves the nest]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/121/OWL-leaves-the-nest</link>
  <description><![CDATA[OWL leaves the nest


a panel at the 

2005 AAAI Fall Symposium Series
First International Symposium on Agents and the Semantic Web
16:00-17:30 Friday 4 November 2005
Hyatt Regency Crystal City, Arlington VA



Panelists

 Tim Finin, UMBC, Baltimore MD (moderator)

Norman Sadeh, CMU, Pittsburgh PA

Yannis Labrou, Fujitsu Laboratories of America, College Park MD

Harry Lik Chen, Image Matters LLC, Leesburg VA

Filip Perich, Shared Spectrum Company, Vienna VA


Althoug...]]></description>
  <dc:date>2005-11-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/913/On-the-Integration-of-Inconsistent-Knowledge-with-Bayseian-Networks">
  <title><![CDATA[On the Integration of Inconsistent Knowledge with Bayseian Networks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/913/On-the-Integration-of-Inconsistent-Knowledge-with-Bayseian-Networks</link>
  <description><![CDATA[Incorporating or integrating new knowledge into existing knowledge bases (KBs) is critical for developing and maintaining the reliability and accuracy thereof. This thesis focuses on integrating pieces of discrete probabilistic knowledge, represented as low dimensional distributions (also called constraints), into an existing Bayesian network (BN) where the probabilistic dependency relations among the variables in these constraints are inconsistent with those captured by the network structure...]]></description>
  <dc:date>2018-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/931/Inconsistent-Knowledge-Integration-with-Bayesian-Network">
  <title><![CDATA[Inconsistent Knowledge Integration with Bayesian Network]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/931/Inconsistent-Knowledge-Integration-with-Bayesian-Network</link>
  <description><![CDATA[Given a Bayesian network (BN) representing a probabilistic knowledge base of a domain, and a set of low-dimensional probability distributions (also called constraints) representing pieces of new knowledge coming from more up-to-date or more specific observations for a certain perspective of the domain, we present a theoretical framework and related methods for integrating the constraints into the BN, even when these constraints are inconsistent with the structure of the BN due to dependencies...]]></description>
  <dc:date>2016-05-16</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/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/resource/html/id/157/OWL-Leaves-the-Nest-Knowledge-Integration-for-Ubiquitious-Agents">
  <title><![CDATA[OWL Leaves the Nest -- Knowledge Integration for Ubiquitious Agents]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/157/OWL-Leaves-the-Nest-Knowledge-Integration-for-Ubiquitious-Agents</link>
  <description><![CDATA[Agents and the Semantic Web, 2005 AAAI Spring Symposium.]]></description>
  <dc:date>2005-11-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/158/OWL-Leaves-the-Nest-Knowledge-Integration-for-Ubiquitious-Agents">
  <title><![CDATA[OWL Leaves the Nest -- Knowledge Integration for Ubiquitious Agents]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/158/OWL-Leaves-the-Nest-Knowledge-Integration-for-Ubiquitious-Agents</link>
  <description><![CDATA[Agents and the Semantic Web, 2005 AAAI Spring Symposium.]]></description>
  <dc:date>2005-11-04</dc:date>
 </item>
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