Title :
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
fDate :
9/1/1992 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
