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 <channel rdf:about="http://ebiquity.umbc.edu//tag/html/bayesian network/?t=bayesian+network">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tag/html/bayesian network/?t=bayesian+network]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for bayesian network]]></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/165/Semantically-Linked-Bayesian-Networks-A-Framework-for-Probabilistic-Inference-Over-Multiple-Bayesian-Networks"/>
      <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/125/Knowledge-discovery-in-networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/150/Activity-recognition-from-RFID-sensor-data"/>
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      <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/71/ArRf-Activity-Recognition-with-RF"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/59/Bayes-OWL"/>
      <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/929/Modify-Bayesian-Network-Structure-with-Inconsistent-Constraints"/>
      <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/528/Integrating-Probability-Constraints-into-Bayesian-Nets"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/529/Bayesian-Network-Reasoning-with-Uncertain-Evidences"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/489/A-Practical-Tool-for-Uncertainty-in-OWL-Ontologies"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/914/BayesOWL-A-Prototype-System-for-Uncertainty-in-Semantic-Web"/>
      <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/534/Knowledge-Based-Systems-and-Other-AI-Applications-for-Tableting"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/256/BayesOWL-binary-file"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/257/BayesOWL-source-file"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/162/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/163/BayesOWL-A-Probabilistic-Framework-for-Uncertainty-in-Semantic-Web-pdf-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks"/>
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 </channel>
 <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/165/Semantically-Linked-Bayesian-Networks-A-Framework-for-Probabilistic-Inference-Over-Multiple-Bayesian-Networks">
  <title><![CDATA[Semantically-Linked Bayesian Networks: A Framework for Probabilistic Inference Over Multiple Bayesian Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/165/Semantically-Linked-Bayesian-Networks-A-Framework-for-Probabilistic-Inference-Over-Multiple-Bayesian-Networks</link>
  <description><![CDATA[At the present time, Bayesian networks (BNs), presumably the most popular uncertainty inference framework, are still widely used as standalone systems. When the problem itself is distributed, domain knowledge has to be centralized and unified before a single BN can be created. Alternatively, separate BNs describing related sub-domains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving even if the interdependent relations between var...]]></description>
  <dc:date>2006-08-02</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/125/Knowledge-discovery-in-networks">
  <title><![CDATA[Knowledge discovery in networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/125/Knowledge-discovery-in-networks</link>
  <description><![CDATA[Networks are an increasing common method of representing the
relationships among sets of interacting entities. This basic
data structure is reflected in how we analyze and understand
social networks, networks of scholarly citations and web
pages, and networks of computers and communications
devices. Over the past five years, my students and I have
developed a number of methods for learning statistical models
of networks that can make accurate predictions about the
attributes of nodes ...]]></description>
  <dc:date>2005-11-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/150/Activity-recognition-from-RFID-sensor-data">
  <title><![CDATA[Activity recognition from RFID sensor data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/150/Activity-recognition-from-RFID-sensor-data</link>
  <description><![CDATA[As the population ages tools for aiding in the care of elderly become increasingly valuable. There is a need for a suite of tools that monitor senior citizens, help them through their day, and alert others if they need help. Several good techniques for creating systems that assist senior citizens have emerged. What all such computer systems lack is a good way to determine what a person is actually doing. Entering every task that a person does into a computer is time consuming and not practica...]]></description>
  <dc:date>2005-09-25</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/event/html/id/65/Bayesian-network-mapping">
  <title><![CDATA[Bayesian network mapping]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/65/Bayesian-network-mapping</link>
  <description><![CDATA[Rong Pan will  give a preview of his disseration proposal.


Expected contribution of my Ph.D. reserach would be a theoretical
framework and related inference methods to map semantically similar
variables between separate Bayesian networks in a provably correct way.
The framework can be used to support automatic mapping of concepts between
ontologies with the help of probability extension of ontologies.]]></description>
  <dc:date>2004-10-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/getnews/html/id/38/Looking-back-at-the-ebiquity-research-group-s-2006">
  <title><![CDATA[Looking back at the ebiquity research group's 2006]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/38/Looking-back-at-the-ebiquity-research-group-s-2006</link>
  <description><![CDATA[Maybe it's a bit of a cliché, but this is the traditional time to look back on the past year and reflect on how things are going.  It has been an active productive year.  Here's a rundown of our past year by the numbers.

205,000 is the number of visits to the Ebiquity web site.  Our monthly page visits increased five fold over the year and we currently receive about 25,000 visits a month.


   



743 people have
registered as users of the Swoogle
semantic web search system.  By ...]]></description>
  <dc:date>2007-01-01</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/71/ArRf-Activity-Recognition-with-RF">
  <title><![CDATA[ArRf - Activity Recognition with RF]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF</link>
  <description><![CDATA[As the population ages tools for aiding in the care of elderly become increasingly valuable.  There is a need for a suite of tools that monitor senior citizens, help them through their day, and alert others if they need help.  Several good techniques for creating systems that assist senior citizens have emerged.  What all such computer systems lack is a good way to determine what a person is actually doing.  Entering every task that a person does into a computer is time consuming and not prac...]]></description>
  <dc:date>2005-09-01</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/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/929/Modify-Bayesian-Network-Structure-with-Inconsistent-Constraints">
  <title><![CDATA[Modify Bayesian Network Structure with Inconsistent Constraints]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/929/Modify-Bayesian-Network-Structure-with-Inconsistent-Constraints</link>
  <description><![CDATA[This paper presents a theoretical framework and related methods for integrating probabilistic knowledge represented as low dimensional distributions (also called constraints) into an existing Bayesian network (BN), even when these constraints are inconsistent with the structure of the BN due to dependencies among relevant variables in the constraints being absent in the BN. Within this framework, a method has been developed to identify structural inconsistencies. Methods have also been develo...]]></description>
  <dc:date>2016-09-26</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/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/529/Bayesian-Network-Reasoning-with-Uncertain-Evidences">
  <title><![CDATA[Bayesian Network Reasoning with Uncertain Evidences]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/529/Bayesian-Network-Reasoning-with-Uncertain-Evidences</link>
  <description><![CDATA[This paper investigates the problem of belief update in Bayesian networks (BN) with uncertain 
evidence. Two types of uncertain evidences are identified:  virtual evidence (reflecting the 
uncertainty one has about a reported observation) and soft evidence (reflecting the uncertainty of an 
event one observes). Each of the two types of evidence has its own characteristics and obeys a belief 
update rule that is different from hard evidence, and different from each other. The particular 
...]]></description>
  <dc:date>2010-06-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/489/A-Practical-Tool-for-Uncertainty-in-OWL-Ontologies">
  <title><![CDATA[A Practical Tool for Uncertainty in OWL Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/489/A-Practical-Tool-for-Uncertainty-in-OWL-Ontologies</link>
  <description><![CDATA[Previously we have proposed a theoretical framework, named BayesOWL, which translates an OWL taxonomy of concept classes into a Bayesian network (BN) and incorporates consistent probabilistic information about the concept classes into the translated BN. In this paper, we extend the original framework to support general OWL DL ontologies and to effectively deal with inconsistent probabilistic information. We have also implemented the BayesOWL prototype system, which can be used as a practical ...]]></description>
  <dc:date>2010-02-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/914/BayesOWL-A-Prototype-System-for-Uncertainty-in-Semantic-Web">
  <title><![CDATA[BayesOWL: A Prototype System for Uncertainty in Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/914/BayesOWL-A-Prototype-System-for-Uncertainty-in-Semantic-Web</link>
  <description><![CDATA[Previously we have proposed a theoretical
framework, called BayesOWL, to model uncertainty in
semantic web ontologies based on Bayesian networks. In
particular, we have developed a set of rules and algorithms to translate an OWL taxonomy into a BN. In this
paper, we describe our implementation of BayesOWL
framework together with examples of its use.]]></description>
  <dc:date>2009-07-01</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/534/Knowledge-Based-Systems-and-Other-AI-Applications-for-Tableting">
  <title><![CDATA[Knowledge-Based Systems and Other AI Applications for Tableting]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/534/Knowledge-Based-Systems-and-Other-AI-Applications-for-Tableting</link>
  <description><![CDATA[The pharmaceutical industry is under continual pressure to speed up the drug development process, reduce costs, and improve process design. At the same time, FDA’s new Process Analytical Technology initiatives encourage the building of product quality and the development of meaningful product and process specifications that are ultimately linked to clinical performance. Together, these two issues present significant challenges to formulation and process scientists because of the complex, ty...]]></description>
  <dc:date>2008-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/256/BayesOWL-binary-file">
  <title><![CDATA[BayesOWL binary file]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/256/BayesOWL-binary-file</link>
  <description><![CDATA[BayesOWL is Java-based tool. It can be used to extract taxonomies from OWL ontologies, translate taxonomies into Bayesian Networks and integrate uncertainty knowledge into BNs.]]></description>
  <dc:date>2008-12-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/257/BayesOWL-source-file">
  <title><![CDATA[BayesOWL source file]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/257/BayesOWL-source-file</link>
  <description><![CDATA[BayesOWL is Java-based tool. It can be used to extract taxonomies from OWL ontologies, translate taxonomies into Bayesian Networks and integrate uncertainty knowledge into BNs.]]></description>
  <dc:date>2008-12-22</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>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks">
  <title><![CDATA[Semantically-Linked Bayesian Networks]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks</link>
  <description><![CDATA[At the present time, Bayesian networks (BNs), presumably the most popular uncertainty inference framework, are still widely used as standalone systems. When the problem itself is distributed, domain knowledge has to be centralized and unified before a single BN can be created. Alternatively, separate BNs describing related sub-domains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving even if the interdependent relations between var...]]></description>
  <dc:date>2006-08-02</dc:date>
 </item>
</rdf:RDF>
