Within the Context Data Cloud project, we have worked on several ontologies.

Context Data Cloud Ontology

The CDC Platform is based on a comprehensive Context Data Cloud Ontology comprising several ontology facets that support various features to be used in sophisticated services. The Linked Crowdsourced Data dataset, for example, is described by using the Location, Context Situation and Additional Information facets, whereas self-referencing as well as cross-referencing proactive LBS are enabled through the User Profile, Tracking and Service facets. This ontology is easily extendable by new facets for incorporating new service features.

Context Meta Ontology

The amount of data within the LOD Cloud is steadily increasing and resembles a rich source of information. Since context-aware services (CAS) are based on the correlation of heterogeneous data sources for deriving the contextual situation of a target, it makes sense to leverage that enormous amount of data already present in the LOD Cloud to enhance the quality of these services.

Inspired by VoID, the Context Meta Ontology provides a meta description for relevant context information contained within a dataset. In the first version, this ontology needs to be populated with information by each dataset owner by categorizing its major vocabulary concepts to diverse context facets including location and time constraints. By doing so, their dataset becomes available for context data discovery provided by the CDC Platform.