A Practical Approach to the Derivation of a Materialized Ontology View

Abstract

The success of the semantic web depends largely on how well ontologies can be utilized and formulated. Interoperability between systems using different versions of the same ontology is essential, and this implies the need for a regulated derivation of materialized ontology views (which can be considered a modified version of an ontology). This chapter applies the formalisms for such a derivation process to a practical example, emphasizing the possibility for automation, and also for optimization, to develop a high-quality derived ontology.

Modeling Ontology Views

“…Therefore, unlike generic view models, views for such RDF (both logical and concrete) have no tangible scope outside its domain. In related area of research, the authors of the work proposed a logical view formalism for ontology [1, 15,42] with limited support for conceptual extensions, where materialized ontology views are derived from conceptual/abstract view extensions.…”

“…Another intriguing difference between database and Ontology base is that, database trend to follow a well-defined and established standard/(s), while Ontology standards, functionality and definitions trend to differ between implementations and models due to its infancy [2,15]. For example, in OWL [16] one can create instances as part of the ontology but not in the DOGMA approach [6].…”

“…This is because ObjectOriented (00) modeling languages (such as UML) provide insufficient modeling constructs for utilizing semi-structured (such as XML, RDF, OWL) schema based data descriptions and constraints, while XML/RDF Schema lacks the ability to provide higher levels of abstraction (such as conceptual models) that are easily understood by humans. To address this issue, many researchers have proposed OMG's UML (and OCL) based solutions [2,15,[18][19][20][21], with added extensions to model semi-structured data.…”

“…Conversely, success of SW and its applications depends heavily on utilization and interoperability of well formulated Ontology bases in an automated, heterogeneous environment. This creates a need to investigate utilization of (materialized) Ontology views [2] in SW applications, such as; (a) Ontology extraction, (b) Ontology versioning, (c) sub-ontology bases, and (d) SW-wrappers for traditional data sources, in industrial settings. However, unlike traditional database systems, high level modeling, design and querying techniques still proves to be a challenging task for SW paradigm as, Ontology view definitions and querying have to be done at high-level abstraction [2,3].…”

“…Conversely, success of SW and its applications depends heavily on utilization and interoperability of well formulated Ontology bases in an automated, heterogeneous environment. This creates a need to investigate utilization of (materialized) Ontology views [2] in SW applications, such as; (a) Ontology extraction, (b) Ontology versioning, (c) sub-ontology bases, and (d) SW-wrappers for traditional data sources, in industrial settings. However, unlike…”