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  <!ENTITY pub "http://ebiquity.umbc.edu/ontology/publication.owl#">
  <!ENTITY assert "http://ebiquity.umbc.edu/ontology/assertion.owl#">]>

<!--
  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.
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  <pub:PhdThesis rdf:about="http://ebiquity.umbc.edu/paper/html/id/427/Constraint-Generation-and-Reasoning-in-OWL">
    <rdfs:label><![CDATA[Constraint Generation and Reasoning in OWL]]></rdfs:label>
    <pub:title><![CDATA[Constraint Generation and Reasoning in OWL]]></pub:title>
    <pub:publishedOn rdf:datatype="&xsd;dateTime">2008-11-17T00:00:00-05:00</pub:publishedOn>
    <pub:abstract><![CDATA[The majority of OWL ontologies in the emerging SemanticWeb are constructed from properties that lack domain and range constraints. Constraints in OWL are different from the familiar uses in programming languages and databases. They are actually type assertions that are made about the individualswhich are connected by the property. Because they are type assertions these assertions can add vital information to the individuals involved and give information on how the defining property may be used. Three different automated generation techniques are explored in this research: disjunction, least-common named subsumer, and vivification. Each algorithm is compared for the ability to generalize, and the performance impacts with respect to the reasoner. A large sample of ontologies from the Swoogle repository are used to compare real-world performance of these techniques. Using generated facts is a type of default reasoning. This may conflict with future assertions to the knowledge base. While general default reasoning is non-monotonic and undecidable a novel approach is introduced to support efficient contraction of the default knowledge.  Constraint generation and default reasoning, together, enable a robust and efficient generation of domain and range constraints which will result in the inference of additional facts and improved performance for a number of Semantic Web applications.]]></pub:abstract>
    <pub:organization><![CDATA[Department of Computer Science and Electrical Engineering]]></pub:organization>
    <pub:counter>561</pub:counter>
    <pub:school><![CDATA[University of Maryland, Baltimore County]]></pub:school>
    <pub:author>
       <rdf:List>
         <rdf:first><person:Alumnus rdf:about="http://ebiquity.umbc.edu/person/html/Tom/Briggs/"><person:name><![CDATA[Tom  Briggs]]></person:name><rdfs:label><![CDATA[Tom  Briggs]]></rdfs:label></person:Alumnus></rdf:first>
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    <pub:firstAuthor><person:Alumnus rdf:about="http://ebiquity.umbc.edu/person/html/Tom/Briggs/"><person:name><![CDATA[Tom  Briggs]]></person:name><rdfs:label><![CDATA[Tom  Briggs]]></rdfs:label></person:Alumnus></pub:firstAuthor>
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      <pub:softCopyFormat><![CDATA[PDF Document]]></pub:softCopyFormat>
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      <pub:softCopyFormat><![CDATA[Microsoft PowerPoint]]></pub:softCopyFormat>
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