Acquisition and Validation of Knowledge from Data

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Citation: Michael Walker and Gio Wiederhold. (1990) Acquisition and Validation of Knowledge from Data. In KSL-90-02, 1990.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Michael Walker and Gio Wiederhold
title Acquisition and Validation of Knowledge from Data
number KSL-90-02
institution Knowledge Systems, AI Laboratory
year 1990
Bibtex more
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abstract Data provide a basis for most of our knowledge. Although most knowledge is obtained through personal experience and education, its direct extraction from databases also has an important role. The process of extracting knowledge from data may take several forms:1. Automated abstraction and summarization; for example, extracting a concise description of a patient's history from a lengthy medical chart2. Discovery of new knowledge about relationships; for example, discovering drug side effects by retrospective examination of medical databases3. Discovery of new abstractions; for example, determining the need for a new factor to explain inconsistencies4. Quantitative knowledge acquisition; for example, deriving likelihood ratios or other statistical parameters for rules in knowledge bases by statistical analysis of a database5. Knowledge validation; for example, monitoring the database to ensure that knowledge-base rules entered in the past remain adequate, or testing the accuracy of new rules proposed by domain experts.In each case, we begin with a database of case examples, and apply a combination of statistical analysis and domain knowledge to extract the knowledge implicitly present in the data. In this chapter, we describe systems that we have implemented to perform these tasks.

KSL Technical Report ID: KSL-90-02
Facts about Acquisition and Validation of Knowledge from DataRDF feed
Abstract Data provide a basis for most of our knowl … Data provide a basis for most of our knowledge. Although most knowledge is obtained through personal experience and education, its direct extraction from databases also has an important role. The process of extracting knowledge from data may take several forms:1. Automated abstraction and summarization; for example, extracting a concise description of a patient's history from a lengthy medical chart2. Discovery of new knowledge about relationships; for example, discovering drug side effects by retrospective examination of medical databases3. Discovery of new abstractions; for example, determining the need for a new factor to explain inconsistencies4. Quantitative knowledge acquisition; for example, deriving likelihood ratios or other statistical parameters for rules in knowledge bases by statistical analysis of a database5. Knowledge validation; for example, monitoring the database to ensure that knowledge-base rules entered in the past remain adequate, or testing the accuracy of new rules proposed by domain experts.In each case, we begin with a database of case examples, and apply a combination of statistical analysis and domain knowledge to extract the knowledge implicitly present in the data. In this chapter, we describe systems that we have implemented to perform these tasks. e have implemented to perform these tasks.
Author Michael Walker and Gio Wiederhold  +
Bibtype techreport  +
Has author Michael Walker and Gio Wiederhold  +
Has identifier KSL-90-02  +
Has publishing details 1990  +
Has title Acquisition and Validation of Knowledge from Data  +
Has where published KSL-90-02  +
Has year 1990  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-90-02  +
Number KSL-90-02  +
Process note YES  +
Title Acquisition and Validation of Knowledge from Data  +
Year 1990  +
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