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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=uncertainty">
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  <description><![CDATA[UMBC ebiquity RSS Tag Search for uncertainty]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/375/Towards-Cooperative-Autonomous-Resilient-Defenses-in-Cyberspace"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/299/Outlier-Detection-in-Ad-Hoc-Networks-Using-Dempster-Shafer-Theory"/>
      <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/155/A-Trust-Based-Framework-for-Secure-Data-Aggregation-in-Wireless-Sensor-Networks"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/51/Uncertainty-in-Ontology-Mapping-A-Bayesian-Perspective"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/5/META-LEVEL-CONTROL-IN-BOUNDED-RATIONAL-AGENTS"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1037/Knowledge-Infused-Learning-A-Sweet-Spot-in-Neuro-Symbolic-AI"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/891/Compressive-Geospatial-Analytics"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/914/BayesOWL-A-Prototype-System-for-Uncertainty-in-Semantic-Web"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/421/An-Efficient-Method-for-Probabilistic-Knowledge-Integration"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/47/Uncertainty-in-Ontology-Mapping-A-Bayesian-Perspective"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/375/Towards-Cooperative-Autonomous-Resilient-Defenses-in-Cyberspace">
  <title><![CDATA[Towards Cooperative Autonomous Resilient Defenses in Cyberspace]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/375/Towards-Cooperative-Autonomous-Resilient-Defenses-in-Cyberspace</link>
  <description><![CDATA[Cyberspace is becoming the nervous system of our modern society as it is increasingly being integrated in virtually all aspects of control and communications. From worldwide social interactions and gaming to smart infrastructure systems in energy, healthcare, transportation, and emergency response, all are enabled and advanced by operations within cyberspace. Today’s cyberspace comprises highly dynamic, interdependent global network of information technology infrastructures, telecommunicati...]]></description>
  <dc:date>2010-10-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/299/Outlier-Detection-in-Ad-Hoc-Networks-Using-Dempster-Shafer-Theory">
  <title><![CDATA[Outlier Detection in Ad Hoc Networks Using Dempster-Shafer Theory]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/299/Outlier-Detection-in-Ad-Hoc-Networks-Using-Dempster-Shafer-Theory</link>
  <description><![CDATA[Mobile Ad-hoc NETworks (MANETs) are known to be vulnerable to a variety of
attacks due to lack of central authority or fixed network infrastructure.
Many security schemes have been proposed to identify misbehaving nodes.
Most of these security schemes rely on either a predefined threshold, or a
set of well-defined training data to build up the detection mechanism
before effectively identifying the malicious peers. However, it is
generally difficult to set appropriate thresholds, and c...]]></description>
  <dc:date>2009-05-06</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/155/A-Trust-Based-Framework-for-Secure-Data-Aggregation-in-Wireless-Sensor-Networks">
  <title><![CDATA[A Trust Based Framework for Secure Data Aggregation in Wireless Sensor Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/155/A-Trust-Based-Framework-for-Secure-Data-Aggregation-in-Wireless-Sensor-Networks</link>
  <description><![CDATA[In unattended and hostile environments, node compromise can
become a disastrous threat to wireless sensor networks and
introduce uncertainty in the aggregation results. A compromise
node often tends to completely reveal its secrets to an adversary
which in turn renders purely cryptography-based approaches
vulnerable. How to secure the information aggregation process
against node compromise attacks and quantify the uncertainty in
the aggregation results has become an important research...]]></description>
  <dc:date>2006-05-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/147/Using-Information-Extraction-to-Automatically-Generate-Probabilistic-Ontologies">
  <title><![CDATA[Using Information Extraction to Automatically Generate Probabilistic Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/147/Using-Information-Extraction-to-Automatically-Generate-Probabilistic-Ontologies</link>
  <description><![CDATA[The Semantic Web is a rapidly developing research area that promises
to deliver Tim Berners-Lee's vision of a world where agents can
communicate, reason, and act to complete complex tasks for their
users.  Ontology languages have evolved as the de facto presentation
language for the Semantic Web.  Today there are over one million
Semantic Web Documents indexed in the Swoogle database collection.
This seemingly impressive number is dwarfed by the more than nine
billion pages in the Goog...]]></description>
  <dc:date>2006-04-25</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/event/html/id/51/Uncertainty-in-Ontology-Mapping-A-Bayesian-Perspective">
  <title><![CDATA[Uncertainty in Ontology Mapping: A Bayesian Perspective]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/51/Uncertainty-in-Ontology-Mapping-A-Bayesian-Perspective</link>
  <description><![CDATA[A presentation at the  I3CON 2004 conference.]]></description>
  <dc:date>2004-08-24</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/5/META-LEVEL-CONTROL-IN-BOUNDED-RATIONAL-AGENTS">
  <title><![CDATA[META-LEVEL CONTROL IN BOUNDED-RATIONAL AGENTS]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/5/META-LEVEL-CONTROL-IN-BOUNDED-RATIONAL-AGENTS</link>
  <description><![CDATA[Autonomous agents must make real-time decisions on the scheduling and
coordination of domain activities. These control decisions are made in
the context of limited resources and uncertainty about action
outcomes. The meta-level control problem is deciding how to sequence
domain and control actions without consuming too many resources in the
process. The state-of-the-art in agent architectures and control
algorithms does not explicitly reason about the cost of time and other
resources...]]></description>
  <dc:date>2003-12-01</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/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/project/html/id/78/Policy-based-Automated-WAN-Configuration-and-Management">
  <title><![CDATA[Policy-based Automated WAN Configuration and Management]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/78/Policy-based-Automated-WAN-Configuration-and-Management</link>
  <description><![CDATA[There are many significant challenges related to configuration and management of wide-area networks due to their rapidly growing complexity, failures, and attacks.  The DARPA Knowledge Plane Study identified that main challenges to be intelligent network management, fault detection, attack response, and fast network configuration.  With uncertainty and threads of battle areas, these challenges further grow exponentially.

In order to overcome the challenges, the project aims at developing a...]]></description>
  <dc:date>2006-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1037/Knowledge-Infused-Learning-A-Sweet-Spot-in-Neuro-Symbolic-AI">
  <title><![CDATA[Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1037/Knowledge-Infused-Learning-A-Sweet-Spot-in-Neuro-Symbolic-AI</link>
  <description><![CDATA[Deep learning has revolutionized the artificial intelligence (AI) landscape by enhancing machine capabilities to understand data-dependant relationships. On the other hand, knowledge may not directly correlate or depend on the data but represents facts that are true. Combining knowledge with the data-driven deep learning techniques improves upon what can be learned from data alone, resulting in improved performance with reduced training, user-level explainability, modeling uncertainty in deep...]]></description>
  <dc:date>2022-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers">
  <title><![CDATA[Leveraging Artificial Intelligence to Advance Problem-Solving with Quantum Annealers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers</link>
  <description><![CDATA[We show how to advance quantum information processing, specifically problem-solving with quantum annealers, in the realm of artificial intelligence.  We introduce SAT++, as a novel quantum programming paradigm, that can compile classical algorithms (implemented in classical programming languages) and execute them on quantum annealers.   Moreover, we introduce a post-quantum error correction method that can find samples with significantly lower energy values, compared to the state-of-the-art t...]]></description>
  <dc:date>2020-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/891/Compressive-Geospatial-Analytics">
  <title><![CDATA[Compressive Geospatial Analytics]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/891/Compressive-Geospatial-Analytics</link>
  <description><![CDATA[Compressive sensing is a randomized data acquisition method that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling theorem, and outperforms traditional signal processing techniques through performing both sensing and size reduction tasks simultaneously. Edge-computing is a decentralization approach that provides several properties (specifically reducing the need for moving a large volume of data) via pushing the computation towards the edge of t...]]></description>
  <dc:date>2019-12-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/845/Quantum-Annealing-Based-Binary-Compressive-Sensing-with-Matrix-Uncertainty">
  <title><![CDATA[Quantum Annealing Based Binary Compressive Sensing  with Matrix Uncertainty]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/845/Quantum-Annealing-Based-Binary-Compressive-Sensing-with-Matrix-Uncertainty</link>
  <description><![CDATA[Compressive sensing is a novel approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate and outperforms traditional signal processing techniques in acquiring and reconstructing such signals. Compressive sensing with matrix uncertainty is an extension of the standard compressive sensing problem that appears in various applications, including but not limited to cognitive radio sensing, calibration of the antenna, and deconvolution. The...]]></description>
  <dc:date>2019-01-01</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/487/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/487/BayesOWL-A-Prototype-System-for-Uncertainty-in-Semantic-Web</link>
  <dc:date>2009-07-15</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/452/Outlier-Detection-in-Ad-Hoc-Networks-Using-Dempster-Shafer-Theory">
  <title><![CDATA[Outlier Detection in Ad Hoc Networks Using Dempster-Shafer Theory]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/452/Outlier-Detection-in-Ad-Hoc-Networks-Using-Dempster-Shafer-Theory</link>
  <description><![CDATA[Mobile Ad-hoc NETworks (MANETs) are known to be vulnerable to a variety of attacks due to lack of central authority or fixed network infrastructure. Many security schemes have been proposed to identify misbehaving nodes. Most of these security schemes rely on either a predefined threshold, or a set of well-defined training data to build up the detection mechanism before effectively identifying the malicious peers. However, it is generally difficult to set appropriate thresholds, and collectin...]]></description>
  <dc:date>2009-05-18</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/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>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/47/Uncertainty-in-Ontology-Mapping-A-Bayesian-Perspective">
  <title><![CDATA[Uncertainty in Ontology Mapping: A Bayesian Perspective]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/47/Uncertainty-in-Ontology-Mapping-A-Bayesian-Perspective</link>
  <dc:date>2004-08-25</dc:date>
 </item>
</rdf:RDF>
