Integration of Contextual Descriptors in Ontology Alignment for Enrichment of Semantic Correspondence

TL;DR

This paper introduces a new semantic ontology alignment method that incorporates contextual descriptors, significantly improving alignment accuracy by capturing the complexity and context-dependence of knowledge, especially in AI-related concepts.

Contribution

The paper presents a formalized approach integrating essential and contextual descriptors into ontology alignment, enhancing semantic correspondence accuracy in complex knowledge domains.

Findings

Average improvement of 4.36% in alignment metrics

Significant enhancement in privacy, responsibility, and autonomy areas

Effective handling of concept conflicts like 'Transparency' and 'Privacy'

Abstract

This paper proposes a novel approach to semantic ontology alignment using contextual descriptors. A formalization was developed that enables the integration of essential and contextual descriptors to create a comprehensive knowledge model. The hierarchical structure of the semantic approach and the mathematical apparatus for analyzing potential conflicts between concepts, particularly in the example of "Transparency" and "Privacy" in the context of artificial intelligence, are demonstrated. Experimental studies showed a significant improvement in ontology alignment metrics after the implementation of contextual descriptors, especially in the areas of privacy, responsibility, and freedom & autonomy. The application of contextual descriptors achieved an average overall improvement of approximately 4.36%. The results indicate the effectiveness of the proposed approach for more accurately reflecting the complexity of knowledge and its contextual dependence.