The Degree of Association between Concepts Focusing on the Viewpoint

For a computer to perform intelligent information processing requires functions that can extract concepts from words, as humans do, and then associate those concepts with related concepts. In order to implement this association function, it is necessary to quantify the degree of association between two concepts. In the present paper, we propose a method for quantifying degree of association focusing on the viewpoint that uses a concept base （a knowledge base that expresses concepts as a collection of pairs, each pair consisting of an attribute word used to describe the concept and a weighting that expresses the word＇s importance）. Here, ＂Viewpoint＂ is the perspective from which a concept is viewed; for example, consider the degree of association between ＂airplane＂ and ＂automobile＂, and the degree of association between ＂airplane＂ and ＂bird＂. From the viewpoint of ＂vehicle＂, ＂airplane＂ and ＂automobile＂ are highly related, while from the viewpoint of ＂flight＂, ＂airplane＂ and ＂bird＂ are highly related. We present herein a comparison of two methods for calculating degree of association focusing on the viewpoint, and demonstrate that the method involving modulation of attribute weightings based on viewpoint results in degree of association calculations that are closer to human senses.

Emotion Judgment System by EEG Based on Concept Base of EEG Features

This paper proposes an emotion judgment system by using an electroencephalogram(EEG)feature concept base with premise of noises included.This method references the word concept association system,which associates one word with other plural words and decides the relationship between several words.In this proposed emotion judgment system,the source EEG is input and 42 EEG features are constructed by EEG data;the data are then calculated by spectrum analysis and normalization.All 2945 EEG data of 4 emotions in the EEG data emotion knowledge base are calculated by the degree of association for getting the nearest EEG data from the EEG feature concept base constructed by 2844 concepts.From the experiment,the accuracy of the proposed system was 55.9%,which was higher than the support vector machine(SVM)method.As this result,the chain structured feature of the EEG feature concept base and the efficiency by the calculation of degree of association for EEG data help reduce the influence of the noise.