Using Ontology-Based Data Access to Enable Context Recognition in the Presence of Incomplete Information

Ontologies play an important role as a semantic layer for data access in various areas such as the Semantic Web, medicine, and enterprise applications. They capture the terminology of an application domain and describe domain knowledge in a machine-processable way. Formal ontology languages additionally provide semantics to these specifications. In contrast to standard database systems, systems for ontology-based data access (OBDA) may thus infer additional information, only implicit in the given data, to answer queries. Moreover, they usually employ the open-world assumption, which means that knowledge that is not stated explicitly in the data and cannot be inferred is neither assumed to be true nor false. This faithfully models the real world and differs from database query answering, where knowledge not present in the data is assumed to be false. All these features make ontologies valuable tools for systems that integrate heterogeneous data sources and need to automatically interpret the data, to support data analysis or to fully-automatedly recognize complex contexts. This has been generally recognized and several standardized ontologies have recently been published [12,14]. However, often, the processed data is