Integration of Contextual Descriptors in Ontology Alignment for Enrichment of Semantic Correspondence
This work addresses the challenge of accurately aligning ontologies to reflect complex, context-dependent knowledge, which is incremental for domains like AI ethics.
The paper tackled the problem of semantic ontology alignment by integrating contextual descriptors, resulting in an average overall improvement of approximately 4.36% in alignment metrics, particularly in areas like privacy and responsibility.
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.