Thesis (M.Sc.)(Computer Science)--University of Pretoria, 2006. / Includes summary. Includes bibliographical references (leaves 151-152). Available on the Internet via the World Wide Web.

Thesis (Ph.D.)--Murdoch University, 2006. / Thesis submitted to the Division of Arts. Includes bibliographical references.

Thesis (M.S.)--California Polytechnic State University, 2009. / Title from PDF title page; viewed on Apr. 21, 2009. "March, 2009." "In partial fulfillment of the requirements for the degree [of] Master of Science in Computer Science." "Presented to the faculty of California Polytechnic State University, San Luis Obispo." Major professor: Franz Kurfess, Ph. D. Includes bibliographical references (p. 40-42). Also available on microfiche.

Thesis (MCIS - Computer and Information Sciences) --AUT University, 2007. / Includes bibliographical references. Also held in print (ix, 74 leaves : ill. ; 30 cm.) in City Campus Theses Collection (T 006.33 WAN)

Mémoire (M.Eng.) -- Université du Québec à Chicoutimi, 1992. / Résumé disponible sur Internet. CaQCU Document électronique également accessible en format PDF. CaQCU

Thesis (doctoral)--Vasa universitet, 1994. / Thesis t.p. laid in. Includes bibliographical references (p. 247-258) and index.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: Analogy, Design, Functional Modeling, Functional Reasoning, Knowledge Representation, Repertory Grid, SME, Structure Mapping Engine, AI in design. Includes bibliographical references (p.106).

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Low-level representations have proven to be good at certain kinds of adaptive learning. High-level representations make effective use of existing knowledge and perform inference well. To promote using both forms of representation cooperatively rather than engaging in the perennial sectarian debate of supporting one paradigm at the expense of the other, this thesis presents a prototype system demonstrating knowledge retention using genetic algorithms and multiple levels of representation and learning. The prototype uses a mid-level of representation and transformations upward and downward for retaining domain-specific knowledge to bridge the gap between the high-level representation and learning and the genetic algorithm level. The thesis begins with an overview of the work, briefly introduces the principles of genetic algorithms, and states an illustrative domain. Then it reviews related work and two supportive systems. After that, it gives a general description of the prototype system's structure, three levels of representation, two transformations, and three levels of learning. Next, it describes methods of implementing the prototype system in some detail. Finally, it shows results with discussion, and points out conclusions and future work. / Master of Science

DL-based ontologies have been widely used as knowledge infrastructures in knowledge management systems and on the Semantic Web. The development of efficient, sound and complete reasoning technologies has been a central topic in DL research. Recently, the paradigm shift from professional to novice users, and from standalone and static to inter-linked and dynamic applications raises new challenges: Can users build and evolve ontologies, both static and dynamic, with features provided by expressive DLs, while still enjoying e cient reasoning as in tractable DLs, without worrying too much about the quality (soundness and completeness) of results? To answer these challenges, this thesis investigates the problem of tractable and quality-guaranteed reasoning for ontologies in expressive DLs. The thesis develops syntactic approximation, a consequence-based reasoning procedure with worst-case PTime complexity, theoretically sound and empirically high-recall results, for ontologies constructed in DLs more expressive than any tractable DL. The thesis shows that a set of semantic completeness-guarantee conditions can be identifed to efficiently check if such a procedure is complete. Many ontologies tested in the thesis, including difficult ones for an off-the-shelf reasoner, satisfy such conditions. Furthermore, the thesis presents a stream reasoning mechanism to update reasoning results on dynamic ontologies without complete re-computation. Such a mechanism implements the Delete-and-Re-derive strategy with a truth maintenance system, and can help to reduce unnecessary over-deletion and re-derivation in stream reasoning and to improve its efficiency. As a whole, the thesis develops a worst-case tractable, guaranteed sound, conditionally complete and empirically high-recall reasoning solution for both static and dynamic ontologies in expressive DLs. Some techniques presented in the thesis can also be used to improve the performance and/or completeness of other existing reasoning solutions. The results can further be generalised and extended to support a wider range of knowledge representation formalisms, especially when a consequence-based algorithm is available.