Dissertation/Thesis Abstract

PR-OWL Decision: A Framework for Decision Making with Probabilistic Ontologies
by Matsumoto, Shou, Ph.D., George Mason University, 2019, 198; 13864553
Abstract (Summary)

Ontologies (in the context of computer science) are engineering artifacts which formally represent knowledge in a domain, and are widely used as an instrument for enabling common understanding of the information among people or software agents. However, very little work can be found in the literature about ontology languages (and related software tools) that simultaneously support decision making under uncertainty and forward/backward compatibility with OWL, a W3C recommendation and the de facto standard for specifying ontologies. PR-OWL, a probabilistic extension to OWL provides the latter, but does not have standardized support for decision making. This work describes the PR-OWL Decision, which adds decision support to PR-OWL while keeping forward/backward compatibility with OWL. It also reports on an implementation of the extension and illustrates its use via case studies. The implementation includes a GUI and reasoning engine for PR-OWL Decision that were developed as part of this research. Both are based on Multi-Entity Decision Graph, an extension of Multi-Entity Bayesian Network for decision-making problems.

Indexing (document details)
Advisor: Laskey, Kathryn B.
Commitee: Costa, Paulo C. G., Ganesan, Rajesh, Wijesekera, Duminda
School: George Mason University
Department: Systems Engineering and Operations Research
School Location: United States -- Virginia
Source: DAI-B 80/11(E), Dissertation Abstracts International
Subjects: Engineering, Artificial intelligence, Computer science
Keywords: Influence diagrams, Multi-entity decision graph, OWL, Probabilistic ontology
Publication Number: 13864553
ISBN: 978-1-392-22355-0
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