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Knowledge acquisition from databases is a research frontier for both data base technology and machine learning (ML) techniques,and has seen sustained research over recent years. It also acts as a link between the two fields,thus offering a dual benefit. Firstly, since database technology has already found wide application in many fields ML research obviously stands to gain from this greater exposure and established technological foundation. Secondly, ML techniques can augment the ability of existing database systems to represent acquire,and process a collection of expertise such as those which form part of the semantics of many advanced applications (e.gCAD/CAM).The major contribution of this thesis is the introduction of an effcient induction algorithm to facilitate the acquisition of such knowledge from databases. There are three typical families of inductive algorithms: the generalisation- specialisation based AQ11-like family, the decision tree based ID3-like family,and the extension matrix based family. A heuristic induction algorithm, HCV based on the newly-developed extension matrix approach is described in this thesis. By dividing the positive examples (PE) of a specific class in a given example set into intersect in groups and adopting a set of strategies to find a heuristic conjunctive rule in each group which covers all the group's positiv examples and none of the negativ examples(NE),HCV can find rules in the form of variable-valued logic for PE against NE in low-order polynomial time. The rules generated in HCV are shown empirically to be more compact than the rules produced by AQ1-like algorithms and the decision trees produced by the ID3-like algorithms. KEshell2, an intelligent learning database system, which makes use of the HCV algorithm and couples ML techniques with database and knowledgebase technology, is also described.

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

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Knowledge Base Refinement is an area of Machine Learning whose primary goal is the automatic detection and correction of errors in faulty expert system's knowledge bases. A very important feature of a refinement system is the mechanism used to select the refinements to be implemented. Since there are usually different ways to fix a fault, most current Knowledge Base Refinement systems use extensive heuristics to choose one or a few alternative refinements from a set of possible corrections. This approach is justified by the intention of avoiding the computational problems inherent in the generation and testing of multiple refinements. On the other hand, such systems are liable to miss solutions. The opposite approach was adopted by the Knowledge Base Refinement system KRUST which proposed many alternative corrections to refine each wrongly-solved example. Although KRUST demonstrated the feasibility of this approach, the potential of multiple refinement generation could not be fully exploited since the system used a limited set of refinement operators in order to contain the number of alternative fixes generated for each fault, and hence was unable to rectify certain kinds of errors. Additionally, the time taken to produce and test a set of refined knowledge bases was considerable for any non-trivial knowledge base. This thesis presents a major revision of the KRUST system. Like its predecessor, the resulting system, STALKER, proposes many alternative refinements to correct each wrongly classified example in the training set. Two enhancements have been made: the class of errors handled by KRUST has been augmented through the introduction of inductive refinement operators; the testing phase of Knowledge Base Refinement has been speeded up considerably by means of a technique based on a Truth Maintenance System (TMS). The resulting system is more effective than other refinement systems because it generates many alternative refinements. At the same time, STALKER is very efficient since KRUST's computationally expensive implementation and testing of refined knowledge bases has been replaced by a TMS-based simulator.

For decades local knowledge has been seen as an important requirement of property valuers in both residential and commercial markets, when undertaking valuations. Following criticisms of valuation methods in the early 1990's local knowledge became a mandatory requirement of professional surveying organisations in the UK. Despite this prominence no formal definition exists as to the nature or content of local knowledge. Based on empirical research involving a valuation experiment, postal questionnaire and depth interviews with residential valuers in England and Wales, this thesis provides a broader understanding of local knowledge in respect of the open market valuation of residential property. The research critically examines the literature relating to both local knowledge and current valuation practice (the environment in which local knowledge is required). Consideration is also given to professional knowledge and learning from a theoretical perspective. Based on the research undertaken, the thesis reinforces the need for practitioners to have knowledge of the area. The thesis highlights that valuers without local knowledge are more likely to produce inaccurate valuations, and may be drawn into errors in the selection of evidence. At the same time they will take longer to produce a valuation. However, different degrees of familiarity exist, which also impacts on the valuation process. A definition of local knowledge is provided, as are the factors which underpin this knowledge in terms of the inspection, selection of evidence and appraisal. The thesis also demonstrates how knowledge generally remains within the local milieu and the barriers to the transportability of valuation knowledge are discussed. Given the willingness of valuers to value in unfamiliar areas, the thesis concludes that a mandatory licensing system should be introduced to remove the problems associated with valuing in unfamiliar areas.

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BACKGROUND: Ethnobotanical knowledge continues to be important for treating illness in many rural communities, despite access to health care clinics and pharmaceuticals. However, access to health care clinics and other modern services can have an impact on the distribution of medical ethnobotanical knowledge. Many factors have been shown to be associated with distributions in this type of knowledge. The goal of the sub-analyses reported in this paper was to better understand the relationship between herbal remedy knowledge, and two such factors, age and social network position, among the Yucatec Maya in Tabi, Yucatan. METHODS: The sample consisted of 116 Yucatec Maya adults. Cultural consensus analysis was used to measure variation in herbal remedy knowledge using competence scores, which is a measure of participant agreement within a domain. Social network analysis was used to measure individual position within a network using in-degree scores, based on the number of people who asked an individual about herbal remedies. Surveys were used to capture relevant personal attributes, including age. RESULTS: Analysis revealed a significant positive correlation between age and the herbal medicine competence score for individuals 45 and under, and no relationship for individuals over 45. There was an insignificant relationship between in-degree and competence scores for individuals 50 and under and a significant positive correlation for those over 50. CONCLUSIONS: There are two possible mechanisms that could account for the differences between cohorts: 1) knowledge accumulation over time; and/or 2) the stunting of knowledge acquisition through delayed acquisition, competing treatment options, and changes in values. Primary ethnographic evidence suggests that both mechanisms may be at play in Tabi. Future studies using longitudinal or cross-site comparisons are necessary to determine the whether and how the second mechanism is influencing the different cohorts.