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Probabilistic Ontology Reference Architecture and Development Methodology

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dc.contributor.advisor Laskey, Kathryn B.
dc.contributor.advisor Costa, Paulo C.G.
dc.contributor.author Haberlin, Richard
dc.creator Haberlin, Richard en_US
dc.date.accessioned 2014-08-28T03:17:38Z
dc.date.available 2014-08-28T03:17:38Z
dc.date.issued 2013-08 en_US
dc.identifier.uri https://hdl.handle.net/1920/8799
dc.description.abstract The use of ontologies is on the rise, as they facilitate interoperability and provide support for automation. Today, ontologies are popular for research in areas such as the Semantic Web, Knowledge Engineering, Artificial Intelligence and knowledge management. However, many real world problems in these disciplines are burdened by incomplete information and other sources of uncertainty which traditional ontologies cannot represent. Therefore, a means to incorporate uncertainty is a necessity. Probabilistic ontologies extend current ontology formalisms to provide support for representing and reasoning with uncertainty. Traditional ontologies provide a hierarchical structure of entity classes and a formal way of expressing their relationships with first-order expressivity, which supports logical reasoning. However, they lack built-in, principled support to adequately account for uncertainty. Applying simple probability annotations to ontologies fails to convey the structure of the probabilistic representation. Similarly, other less expressive probability schemes do not convey the ontology structure, and are also inadequate. Representation of uncertainty in real-world problems requires probabilistic ontologies, which integrate the inferential reasoning power of probabilistic representations with the first-order expressivity of ontologies. Developing a probabilistic ontology is more complex than simply assigning probability to a class instantiation or representing a probability scheme using ontology constructs. Standard ontological engineering methods provide insufficient support for the complexity of probabilistic ontology development. Therefore, a specific methodology is needed to develop probabilistic ontologies from conceptualization to implementation. This dissertation introduces a systematic approach to probabilistic ontology development facilitated through a reference architecture which focuses on evolving a traditional ontology from conceptualization to probabilistic ontology implementation for real-world problems. The Reference Architecture for Probabilistic Ontology Development captures, catalogues and defines the components necessary for probabilistic ontology development. It includes an efficient, teachable, and repeatable Probabilistic Ontology Development Methodology for the development, implementation and evaluation of explicit, logical and defensible probabilistic ontologies developed for knowledge-sharing and reuse in a given domain.
dc.format.extent 386 pages en_US
dc.language.iso en en_US
dc.rights Copyright 2013 Richard Haberlin en_US
dc.subject Operations research en_US
dc.subject Artificial intelligence en_US
dc.subject Knowledge Engineering en_US
dc.subject Probabilistic Ontology en_US
dc.subject Reference Architecture en_US
dc.title Probabilistic Ontology Reference Architecture and Development Methodology en_US
dc.type Dissertation en
thesis.degree.level Doctoral en
thesis.degree.discipline Systems Engineering en
thesis.degree.grantor George Mason University en


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