Ontology Based Information Integration: A Survey

TL;DR

This survey reviews ontology matching techniques used for information integration across various fields, highlighting their methods, common challenges, and applications in data, schema, catalog, and semantic integration.

Contribution

It provides a comprehensive overview of ontology matching solutions across multiple domains, emphasizing their techniques and integration applications.

Findings

Multiple ontology matching techniques leverage structure, semantics, and labels.

Common challenges include handling heterogeneity and scalability.

Applications span database, semantic web, and e-commerce domains.

Abstract

An ontology makes a special vocabulary which describes the domain of interest and the meaning of the term on that vocabulary. Based on the precision of the specification, the concept of the ontology contains several data and conceptual models. The notion of ontology has emerged into wide ranges of applications including database integration, peer-to-peer systems, e-commerce, semantic web, etc. It can be considered as a practical tool for conceptualizing things which are expressed in computer format. This paper is devoted to ontology matching as a mean or information integration. Several matching solutions have been presented from various areas such as databases, information systems and artificial intelligence. All of them take advantages of different attributes of ontology like, structures, data instances, semantics and labels and its other valuable properties. The solutions have some common techniques and cope with similar problems, but use different methods for combining and exploiting their results. Information integration is among the first classes of applications at which matching was considered as a probable solution. Information integration contains many fields including, data integration, schema integration, catalogue integration and semantic integration. We cover these notions in term of ontology in our proposed paper.