CAMELOT is a semantic data model in development at the Bodleian Digital Library, in which semantic information is included, and that has the capability to express information that enables parties to interpret meaning (semantics) from the instances.

The CAMELOT data model has a modular organisation, with each module concerned with the semantics of a particular domain, e.g. types of educational activity.

The individual modules are defined using the W3Cs Ontology Web Language (OWL) and consist of entity classes, representing kinds of things of significance in the domain, as well as assertions about relationships between pairs of entity classes.

Each of the semantic data models specifies the kinds of facts or assertions that can be expressed using the model, and define the allowed assertions in a machine-readable language.


Context is the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood. A context would include the following:

  • the type of activity
  • the participants in the activity
  • the roles of the participants in the activity (if relevant)
  • time of the event, either a time instant or a time duration
  • location of the event (derived from the location of the participants)


Provenance is a specific kind of context that describes the entities and activities involved in creating, owning or influencing an object.

Activities that are relevant to an object’s provenance include:

  • changes in ownership
  • transfer or declaration of rights
  • creation of an object
  • derivation of an object
  • revision of a digital object
  • use of a digital object

From (Gill et al, 2013):

Provenance can be used for many purposes, such as understanding how data was collected so it can be meaningfully used, determining ownership and rights over an object, making judgements about information to determine whether to trust it, verifying that the process and steps used to obtain a result complies with given requirements, and reproducing how something was generated.


Further Reading

Gill, Y., Miles, S. (2013) PROV Model Primer. Available: