Revisiting Indirect Ontology Alignment : New Challenging Issues in Cross-Lingual Context

TL;DR

This paper introduces a novel indirect ontology alignment method for cross-lingual multilingual ontologies, leveraging alignment algebra to improve efficiency and quality in heterogeneous data integration.

Contribution

It proposes a new indirect alignment approach based on composition and reuse of direct alignments, addressing challenges in multilingual ontology integration.

Findings

Encouraging experimental results validate the effectiveness of the proposed method.

The approach enhances alignment quality and efficiency in cross-lingual contexts.

Alignment algebra effectively governs semantic composition and confidence values.

Abstract

Ontology alignment process is overwhelmingly cited in Knowledge Engineering as a key mechanism aimed at bypassing heterogeneity and reconciling various data sources, represented by ontologies, i.e., the the Semantic Web cornerstone. In such infrastructures and environments, it is inconceivable to assume that all ontologies covering a particular domain of knowledge are aligned in pairs. Moreover, the high performance of alignment approaches is closely related to two factors, i.e., time consumption and machine resource limitations. Thus, good quality alignments are valuable and it would be appropriate to exploit them. Based on this observation, this article introduces a new method of indirect alignment of ontologies in a cross-lingual context. Indeed, the proposed method deals with alignments of multilingual ontologies and implements an indirect ontology alignment strategy based on a composition and reuse of effective direct alignments. The trigger of the proposed method process is based on alignment algebra which governs the semantics composition of relationships and confidence values. The obtained results, after a thorough and detailed experiment are very encouraging and highlight many positive aspects about the new proposed method.