Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 178-185). Also available via the Internet.

Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.

Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.

Thesis (Ph. D.)--University of New South Wales, 2003. / Also available online.

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 120-133).

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 141-147). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S.)--Rochester Institute of Technology, 1990. / Spine title: Identify generic tasks. Includes bibliographical references (leaves 55-57).

Interactive as well as Automatic Verification and Validation is valuable, especially when the size of a knowledge base grows and manual techniques are not feasible. It ensures the stability of the system and raises the confidence in its level of performance. In this dissertation I address the problem of verification and validation of rule based expert systems. It is a problem knowledge engineers have to deal with while building their expert systems to ensure the reliability, accuracy, and completeness of their knowledge bases. The objective of this research is to make it easy for expert systems developers to build the right system by proposing practical and simple methods for building verification and validation programs to insure the integrity and performance of large scale knowledge based systems.

Data mining algorithms discover knowledge from data. The knowledge are commonly expressed as dependency relationships in various forms, like rules, decision trees and Bayesian Networks (BNs). Moreover, many real-world problems are multi-class problems, in which more than one of the variables in the data set are considered as classes. However, most of the rule learners available were proposed for single-class problems only and would produce cyclic rules if they are applied to multi-class ones. In addition, most of them produce rules with conflicts, i.e. more than one of the rules classify the same data items and different rules have different predictions. Similarly, existing decision trees learners cannot handle multi-class problems, and thus cannot detect and avoid cycles. In contrast, BNs represent acyclic dependency relationships among variables, but they can handle discrete values only. They cannot manage continuous, interval and ordinal values and cannot represent higher-order relationships. Consequently, BNs have higher network complexity and lower understandability when they are used for such problems. / This thesis has studied in depth discovering dependency relationships in various forms by Evolutionary Computation (EC). Through analysis of the reasons leading to the disadvantages of rules, decision trees and BNs, and their learners, we have proposed a sequence of EAs, a novel functional dependency network (FDN) and two techniques for dependency relationship learning and for multi-class problems. They are the multi-population Genetic Programming (GP) using backward chaining procedure and the GP employing co-operating scoring stage for acyclic rules learning. The dependency network with functions can manage all kinds of values and represent any kind of relationships among variables, the flexible and robust MDLGP to learn the novel dependency network and BN. Based on the FDN we have further developed the techniques to learn rules without conflict and acyclic decision trees for multi-class problems respectively. The new self-organizing map (SOM) with expanding force for clustering and data visualization for data preprocessing have also been given in the appendix. / Shum Wing Ho. / "May 2007." / Adviser: Kwong-Sak Leung. / Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0436. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 221-240). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

An extension of wrapper adaptation is developed to collectively extract information from multiple Web pages. There exists mutual influence between text fragments of different Web pages and hence they should be considered collectively during extraction. Extending from the dependence model, a framework which can consider the dependence between text fragments within a single Web page and the dependence between text fragments from different pages. One characteristic of this model is that additional information can be incorporated into the model and multiple tasks earl be tackled simultaneously. As a result, a global solution which can optimize the quality of the tasks, at the same time, eliminate the conflict between them can he obtained. Experiments on product feature extraction and hot item mining from multiple auction Web sites have been conducted to demonstrate the effectiveness of this framework. / One problem of most existing Web information extraction methods is that the extraction knowledge learned from a Web site can only be applied to Web pages from the same site. This thesis first investigates the problem of wrapper adaptation which aims at adapting a wrapper previously learned from a source site to new unseen sites. A dependence model that can model the dependence between text fragments in Web pages is developed. Under this model, two types of text related features are identified. The first type of features is called site invariant features. These features likely remain unchanged in Web pages from different sites in the same domain. The second type of features is called site dependent features. These features are different in Web pages collected from different Web sites, while they are similar in Web pages originated from the same site. Based on this model, two frameworks are developed to solve the wrapper adaptation problem. The first framework is called Information Extraction Knowledge Adaptation using Machine Learning approach (IEKA-ML). Machine learning methods are employed to derive site invariant features from the previously learned extraction knowledge and items previously collected or extracted from the source Web site. Both site dependent features and site invariant features in new sites are considered for learning of new information extraction knowledge tailored to the new unseen site. / The second framework, called Information Extraction Knowledge Adaptation using Bayesian learning approach (IEKA-BAYES), solves the problem of wrapper adaptation as well as the issue of new attribute discovery. The new attribute discovery problem aims at extracting new or previously unwell attributes that are not specified in the wrapper. To harness the uncertainty, a probabilistic generative model for the generation of text fragments and layout format related to attributes in Web pages is designed. Bayesian learning and expectation-maximization (EM) techniques are developed under the proposed generative model to accomplish the wrapper adaptation task. Previously unseen attributes together with their semantic labels earl be discovered via another EM-based Bayesian learning on the generative model. Extensive experiments on over 30 real-world Web sites in three different domains and comparison between existing works have been conducted to evaluate the IEKA-ML and IEKA-BAYES frameworks. / Wong Tak Lam. / "October 2006." / Adviser: Lam Wai. / Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6095. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 126-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.