Masters Theses

Abstract

"An ontology can be used to represent and organize the objects, properties, events, processes, and relations that embody an area of reality [1]. These knowledge bases may be created manually (by individuals or groups), and/or automatically using software tools, such as those developed for information retrieval and data mining. Recently, the National Science Foundation funded a large collaborative development project for the semi-automated construction of an ontology of amphibian anatomy (AmphibAnat [2]). To satisfy the extensive community curation requirements of that project, a generic, Web-based, multi-user, relational database ontology management system (RDBOM [3]) was constructed, based upon a novel theoretical ontology model called an Ontology Abstract Machine (OAM [4]). The need to support concurrent data entry by multiple users with different levels of access privileges (as determined and assigned by the administrators), made it critical to ensure that the entered data were semantically correct. In particular, the ability to define and enforce restrictions on property characteristics such as the domain and range of a relation provide several advantages. It helps to identify inconsistencies in the ontology, maintain a higher level of overall integrity, and avoid erroneous conclusions that could be made by automated reasoners. In this thesis a modified OAM model is presented that includes definitions for property characteristics and the associated validation algorithms. As proof of concept, it is shown how this modified abstract model has been implemented for domain and range restrictions in RDBOM"--Abstract, page iii.

Advisor(s)

Leopold, Jennifer

Committee Member(s)

Lin, Dan
Frank, Ronald L.

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Sponsor(s)

National Science Foundation (U.S.)

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2010

Pagination

xi, 54 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2010 Patrick Garrett Edgett, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Expert systems (Computer science)
Ontology
Relational databases -- Management

Thesis Number

T 9725

Print OCLC #

731030443

Electronic OCLC #

745914307

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