Abstract
Achieving semantic interoperability is a current challenge in the field of data integration in order to bridge semantic conflicts occurring when the participating sources and receivers use different or implicit data assumptions. Providing a framework that automatically detects and resolves semantic conflicts is considered as a daunting task for many reasons, it should preserve the local autonomy of the integrated sources, as well as provides a standard query language for accessing the integrated data on a global basis. Many existing traditional and ontology-based approaches have tried to achieve semantic interoperability, but they have certain drawbacks that make them inappropriate for integrating data from a large number of participating sources. We propose semantic conflicts reconciliation (SCR) framework, it is ontology-based system in which all data semantics explicitly described in the knowledge representation phase and automatically taken into account through the interpretation mediation service phase, so conflicts detected and resolved automatically at the query time.
Highlights
Despite the fact that a typical large organization spends nearly 30% of its IT budget on integration and interoperation related efforts, many inter- and intra- organizational systems still have poor interoperability [10]
Technologies already exist to overcome the heterogeneity in hardware, software, and syntax that is used in different systems .While these capabilities are essential to information integration, they do not address the issue of heterogeneous data semantics that exist both within and across enterprises [11]
We developed an ontology-based approach, in which all data semantics explicitly described in the knowledge representation phase and automatically taken into account by Interpretation Mediation Services phase, conflicts detected and resolved automatically at the query runtime
Summary
Despite the fact that a typical large organization spends nearly 30% of its IT budget on integration and interoperation related efforts, many inter- and intra- organizational systems still have poor interoperability [10]. Contextual heterogeneity occurs when different systems (sender/receiver) make different assumptions about the representation of the same concept, such as the profit of a company can be represented in DEM (i.e., Deutschmarks) in one system or in USD (i.e., U.S dollars) in another, where the currency used is the assumption. Ontological heterogeneity occurs when different meanings denoted by the same term (e.g., whether the profit is gross profit including taxes or net profit excluding taxes) because there is a definitional conflicts concerning the inclusion or exclusion of TABLE II. Temporal vs Atemporal heterogeneity [4].tax in the profit Both the representational and the ontological assumptions can be static and do not change over time within an interested time period, in which case time is not of concern. There should be systematic approaches in order to reconcile semantic heterogeneity among heterogeneous sources and receivers
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