Combined use of correlation measures for selecting semantically close concepts of the ontology

Abstract

The paper suggests a new approach to the selection of correlated concepts for the ontology. It is based on the principal component analysis, but, unlike the standard approach, not Pearson correlation coefficients, but other correlation measures are used. This is due to the fact that the selection of concepts is based on data on the semantic association between concepts and cases, which are represented in the form of weight coefficients that take discrete values and a significant number of zero values. For such cases, the most appropriate is the polychoric correlation coefficient. It allows one to detect a monotonous dependence on the contingency table. However, for a certain table structure, the coefficient erroneously indicates a close relationship. This problem has been analysed in detail, and it has been suggested to use the correlation ratio in problem cases. Using the example of the problem of selecting concepts for the ontology in the IT consulting practice, the advantages of the proposed approach are shown. The first oneis the increasein the percentage of variance of concepts explained by the principal components. The second one is that more concepts are selected based on unsupervised feature selection using weighted principal components.