<?xml version="1.0" encoding="UTF-8" ?>
<rdf:RDF
 xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
 xmlns="http://purl.org/rss/1.0/"
 xmlns:dc="http://purl.org/dc/elements/1.1/"
 xmlns:cc="http://web.resource.org/cc/"
 >
<!--
  This ontology document is licensed under the Creative Commons
  Attribution License. To view a copy of this license, visit
  http://creativecommons.org/licenses/by/2.0/ or send a letter to
  Creative Commons, 559 Nathan Abbott Way, Stanford, California
  94305, USA.
-->
 <channel rdf:about="http://ebiquity.umbc.edu/tag/uncertainty/">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <image rdf:resource="http://ebiquity.umbc.edu/img/logo.jpg" />  <title><![CDATA[RSS Tag Search]]></title>
  <link>http://ebiquity.umbc.edu/tag/uncertainty/</link>
  <description><![CDATA[RSS Tag Search]]></description>
  <items>
   <rdf:Seq>
    <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/249/A-Bayesian-Network-Approach-to-Ontology-Mapping"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/271/BayesOWL-Uncertainty-Modeling-in-Semantic-Web-Ontologies"/>
    <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/paper/html/id/281/A-Bayesian-network-based-framework-for-multi-criteria-decision-making"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/92/A-Probabilistic-Extension-to-Ontology-Language-OWL"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/67/Fuzzy-Clustering-for-Intrusion-Detection"/>
    <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"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web"/>
    <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:Seq>
  </items>
 </channel>
 <image rdf:about="http://ebiquity.umbc.edu/img/logo.jpg">
  <title>UMBC ebiquity research group</title>
  <link>http://ebiquity.umbc.edu</link>
  <url>http://ebiquity.umbc.edu/img/logo.jpg</url>
 </image>
 <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/249/A-Bayesian-Network-Approach-to-Ontology-Mapping">
  <title><![CDATA[A Bayesian Network Approach to Ontology Mapping]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/249/A-Bayesian-Network-Approach-to-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. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BN. Probabilities needed for constructing conditional probability tables (CPT...]]></description>
  <dc:date>2005-11-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/271/BayesOWL-Uncertainty-Modeling-in-Semantic-Web-Ontologies">
  <title><![CDATA[BayesOWL: Uncertainty Modeling in Semantic Web Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/271/BayesOWL-Uncertainty-Modeling-in-Semantic-Web-Ontologies</link>
  <description><![CDATA[It is always essential but di±cult to capture incomplete, partial or uncertain
knowledge when using ontologies to conceptualize an application domain or to
achieve semantic interoperability among heterogeneous systems. This chapter
presents an on-going research on developing a framework which augments and
supplements the semantic web ontology language OWL for representing and
reasoning with uncertainty based on Bayesian networks (BN), and its
application in ontology mapping.]]></description>
  <dc:date>2005-10-28</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/paper/html/id/281/A-Bayesian-network-based-framework-for-multi-criteria-decision-making">
  <title><![CDATA[A Bayesian network based framework for multi-criteria decision making]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/281/A-Bayesian-network-based-framework-for-multi-criteria-decision-making</link>
  <description><![CDATA[Multi-Criteria Decision Making (MCDM) involves the selection of
the best actions from a set of alternatives, each of which is
evaluated against multiple, and often conflicting, criteria. Most
of the existing MCDM methods only focus on decisions under
certainty. The criteria were evaluated separately as if they were
independent of each other. Complex, often uncertain interactions
between criteria, and between criteria and other factors are not
modeled in a coherent and systematic manner...]]></description>
  <dc:date>2004-08-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/92/A-Probabilistic-Extension-to-Ontology-Language-OWL">
  <title><![CDATA[A Probabilistic Extension to Ontology Language OWL]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/92/A-Probabilistic-Extension-to-Ontology-Language-OWL</link>
  <description><![CDATA[To support uncertain ontology representation and ontology
reasoning and mapping, we propose to incorporate
Bayesian networks (BN), a widely used graphic model
for knowledge representation under uncertainty and OWL,
the de facto industry standard ontology language recommended
by W3C. First, OWL is augmented to allow
additional probabilistic markups, so probabilities can be
attached with individual concepts and properties in an
OWL ontology. Secondly, a set of translation rules is
defi...]]></description>
  <dc:date>2004-01-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/67/Fuzzy-Clustering-for-Intrusion-Detection">
  <title><![CDATA[Fuzzy Clustering for Intrusion Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/67/Fuzzy-Clustering-for-Intrusion-Detection</link>
  <description><![CDATA[The newly formed Department of Homeland Security has been mandated to reduce America's vulnerability to terrorism. In addition to being charged with physical protection, this newly formed department is also responsible for protecting the nation's critical infrastructure. Protecting computer systems from intrusions is an important aspect of securing the nation's infrastructure. We are exploring how fuzzy data mining and concepts introduced by the semantic Web can operate in synergy to perform ...]]></description>
  <dc:date>2003-04-30</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>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web">
  <title><![CDATA[Learning the Semantic Meaning of a Concept from the Web]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/166/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web</link>
  <description><![CDATA[Many researchers have applied text classification techniques to the ontology mapping problem. The mapping results in these researches heavily depend on the availability of highly relevant text exemplars associated with individual concepts. However, manual preparation of exemplars is costly. In this work, we propose to automatically collect text exemplars by downloading and processing web pages listed in the search results obtained by querying a search engine. Search queries are formed for eac...]]></description>
  <dc:date>2006-08-03</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>
</rdf:RDF>
