Semantic Enrichment in Ontologies for Matching

Abstract

Matching (or mapping) between heterogeneous ontologies becomes crucial for interoperability in distributed and intelligent environments. Although many efforts in ontology mapping have already been conducted, most of them rely heavily on the meaning of entity names rather than the semantics defined in ontologies. In order to deal with semantic heterogeneity, we enrich the semantics of ontologies for content-based matching. In this paper, we propose a semantically-enriched model of ontologies (called MetaOntoModel) where the semantics of concepts are enriched by adding concept-level knowledge (called meta-knowledge) based on three philosophical notions: identity, rigidity, and dependency. Then, we develop a MetaOntoModel-based ontology matching method. Our novel idea is that if two concepts are semantically equivalent, then they have the same metaknowledge. On the contrary, if two concepts possess different kinds of meta-knowledge, then they cannot be matched. We prove that meta-knowledge can determine not only the scope of matches, but also the closest corresponding properties between two similar concepts.

Context-rich semantic framework for effective data-to-decisions in coalition networks

“…For example, authors' of 22 have proposed a mechanism to create a mapping between heterogeneous ontologies in support of interoperability in distributed and intelligent environments. They argue that instead of focusing on the semantic of the term meaning, one should consider the semantics of concepts.…”

An exploratory classification of applications in the realm of collaborative modeling and design

“…Two main approaches have been proposed for model mapping, namely: instance-level model mapping and schema-level model mapping. We will introduce each of these models in detail below: 2.1.2.1 Instance-level model mapping Some model mapping techniques employ instance data to mine the relationships between different models (Tun 2006). This type of model mapping is possible in domains where instance data corresponding to each model exists such as database schemas, but they are not so useful in domains like UML diagram development because instance data cannot be generated for software models.…”