A Hybrid Ontology Mediation Approach for the Semantic Web

Abstract

This article introduces a hybrid ontology mediation approach for the Semantic Web. It combines both syntactic and semantic matching measures to provide better results for matching data labels. Although ontologies are meant to provide a shared conceptualization of the world, the development practices, lack of standards, and subjective naming conventions today, create data heterogeneity problems among ontologies. This is a significant problem particularly for ontologies of similar domains. Mediation techniques at present focus mainly on syntactic matching and our premise is that a hybrid approach would be a better solution to this problem. We provide empirical evidence with hypothesis tests and also provide several new measures such as relevance, reliability, and precision to validate our approach. We also introduce a detailed mapping algorithm.

Achieving interoperability in e-government services with two modes of semantic bridging: SRS and SWRL

“…In this section we present the process methodology for semantic bridging (see figure 4) [13], [15]. The first step is called ontology development.…”

“…Semantic mediation is based on meanings unlike syntactic matching that is just based on string, prefix and suffix matching of schemas [15]. Thus, a less labour-intensive approach for schema integration is needed.…”

“…This would be analogous to developing a global ontology with a global schema [1], [3]. A more practical approach would be to focus on creating a semantic bridge between domain specific local ontologies [14], [15]. [16], [17].…”

“…We use the following symbols: 1) C for concept or class, 2) c for attributes or slots and 3) O for ontology, for simplicity [15]. The tests for E, IC, CN and D were based on definitions in [11].…”

A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

“…Note that the issue of integrating structural and semantic similarity evaluation has also been investigated in the contexts of schema matching/integration [3,4,18], as well as ontology mapping/mediation [46,51,52]. Yet, while comparable to tree-based XML documents, schemas and ontologies often underline more intricate graph structures, and thus require graph-based algorithms and heuristics in evaluating similarity, which are out of the scope of this paper (here, we limit our presentation to XML tree-based approaches).…”