Seminar: B-Annot: Generic and Dataset-Specific Background Annotation of Linked Data Vocabularies, PURO: Providing Deeper Ontological Background to OWL ontologies and Linked Data Vocabularies, Typed Higher-Order Description Logics and its PURO Compliance

Annotation of ontologies/vocabularies by background models is a novel task that requires dedicated tool support. B-Annot is a Protégé plugin that allows to create and store annotations, currently with respect to the PURO model (but potentially also for other models such as OntoClean). It can be used to support annotation of a vocabulary not only generically (based on internal structure, verbal annotations and additional info in a published specification) but also ‘as it is used in a particular dataset’. B-Annot’s advance feature in this respect is the exploitation of dataset summaries retrieved from LODstats.

The surface (or, foreground) structure of OWL ontologies and linked data vocabularies can be complemented by background models expressing valid ontological distinctions that may have become obscured by the modeling style chosen by the vocabulary designer. Background models can generally serve for debugging, visualization, matching, or even pattern-based design of ontologies and vocabularies. An example of a well-known background model language, primarily suited for taxonomic ontologies, is the system of OntoClean meta-properties. We present an alternative type of background model language, dubbed PURO, which is oriented towards linked data ontologies, and relies on particular-universal and relationship-object dichotomies. Typical ‘foreground’ manifestations of background language terms are then discussed.

We introduce a typed higher-order description logic specifically intended for reasoning about ontological coherence of domain models, motivated by a practical use case from linked data vocabularies – the PURO model of background distinctions.