Representation and Reasoning About Changing Semantics in Heterogeneous Data Sources

Abstract

Changes of semantics in data sources further complicate the semantic heterogeneity problem. We identify four types of semantic heterogeneities related to changing semantics and present a solution based on an extension to the Context Interchange (COIN) framework. Changing semantics is represented as multi-valued contextual attributes in a shared ontology; however, only a single value is valid over a certain time interval. A mediator, implemented in abductive constraint logic programming, processes the semantics by solving temporal constraints for single-valued time intervals and automatically applying conversions to resolve semantic differences over these intervals. We also discuss the scalability of the approach and its applicability to the Semantic Web.

Social Support for Ontological Mediation and Data Integration

“…Goh identified three main sources of semantic heterogeneity (Goh, 1997): confounding conflicts, where information items seem to mean the same things, but they are actually different; scaling conflicts, where different standards or references are used to describe the same thing and finally naming conflicts, where it is possible to use synonymous or broader names for the same thing.…”

Semantic Interoperability of Geospatial Services

“…However, when the representation is complex, for example, spatial databases, simple semantic translation is inadequate as it can result in semantic conflicts. Three different types of semantic conflicts have been identified in spatial applications (Goh, 1997): confounding conflicts, scaling conflicts, and naming conflicts. Confounding conflicts occur when information items have the same meaning but have different realizations.…”