Recently, ontology has been recognized as one of the most suitable global conceptual models for information integration architecture due to its easy taxonomical classification of data model and plentiful support of integrity constraint. However, the concept of mapping from global ontology to local information systems depends mostly on the simple metadata structures that allow for the mapping elements to be extracted with an If-Then-Else comparison statement. This kind of mapping is not suitable for ontology based data model in which the concepts are in the multiple subsumption relations. That is, there needs to be a semantic concept mapping in the case of a global concept that is to be mapped to the most specialized/generalized local concept in multiple Is-A structure, which cannot be mapped with simple direct one to one mapping. This kind of mapping needs inference mechanism to map one concept to substantially many target concepts in the concept inclusion hierarchy for an effective semantic query rewriting/optimization. In this paper, we provide an innovative method for semantic ontology concept mapping using Metadata-Based Logic (MBL) approach, which is equipped with knowledge inference mechanism so that the mapping elements can be reasoned automatically. We present semantic mapping patterns to accommodate subsumption problem and detect incoherence for a given global query. The experimental results gave viable results on the semantic query rewriting/optimization.
|Number of pages
|International Journal of Software Engineering and Knowledge Engineering
|Published - 2004 Oct
Bibliographical noteFunding Information:
This work has been supported by KOrea Science and Engineering Foundation (KOSEF) grant R01-2000-000-00404-0 from the year 2002–2003.
- Description logics (DL)
- Information integration system
- Knowledge base
- Metadata-Based Logic (MBl) approach
- Ontology mapping
- Query rewriting/optimization
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Graphics and Computer-Aided Design
- Artificial Intelligence