A neural net for extracting knowledge from natural language data bases

Author

Rocha, A.F. ; Guilherme, I.R. ; Theoto, M. ; Miyadahira, A.M.K. ; Koizumi, M.S.

Author_Institution

Res. on Natural & Artificial Intelligence, Jundia, Brazil

Volume

3

Issue

5

fYear

1992

fDate

9/1/1992 12:00:00 AM

Firstpage

819

Lastpage

828

Abstract

A model of a fuzzy neuron, one which increases the computational power of the artificial neuron, turning it also into a symbolic processing device, is presented. The model proposes the synapsis to be symbolically and numerically defined, by means of the assignment of tokens to the presynaptic and postsynaptic neurons. The matching or concatenation compatibility between these tokens is used to decide about the possible connections among neurons of a given net. The strength of the compatible synapsis is made dependent on the amount of the available presynaptic and postsynaptic tokens. The symbolic and numeric processing capacity of the new fuzzy neuron is used to build a neural net (JARGON) to disclose the existing knowledge in natural language databases such as medical files, sets of interviews and reports about engineering operations

Keywords

database management systems; fuzzy logic; knowledge acquisition; knowledge based systems; natural languages; neural nets; symbol manipulation; JARGON; fuzzy neuron; knowledge extraction; medical files; natural language databases; neural net; numeric processing; postsynaptic neurons; presynaptic neurons; symbolic processing; token assignments; Artificial neural networks; Biomedical engineering; Data mining; Databases; Fuzzy neural networks; Fuzzy sets; Natural languages; Neural networks; Neurons; Turning;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.159072

Filename

159072