Work

Acquisition of knowledge from data

Year: 1986

Type: conference-paper

Abstract: The work described here addresses two problems: information overload of database users, and knowledge acquisition for use in Al systems. We have implemented programs that use artificial intelligence techniques to prepare high-level, intelligent summaries of databases, and that use empirical databases in turn, in combination with statistical and Al methods, to generate new domain knowledge base. Both programs are examples of the aquisition of knowledge from data: the Summarization Module fuses large amounts of data succinctly, the Discovery Module extracts new knowledge present implicitly in data. We describe the implementation of our programs and outline planned extensions which combine both approaches. This work is distinguished from current knowledge engineering approaches in that we prime the system with expert knowledge, and then use factual data to learn more about the domain.

Source: International Syposium on Methodologies for Intelligent Systems

DOI: 10.1145/12808.12817

Authors: G C M Wiederhold, M Walker, R Blum, S Downs

Institution: Stanford University

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, National Institutes of Health, Advanced Research Projects Agency, U.S. National Library of Medicine

Award: Representation and Use of Causal Relationships for Inferencefrom Databases (Information Science)