Share  Email  Print

Prov Ontology

Last edited by kashif at 24/08/2017 2:58 PM

The PROV Ontology (PROV-O) expresses the PROV Data Model [PROV-DM] using the OWL2 Web Ontology Language (OWL2) [OWL2-OVERVIEW]. It provides a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains. 

The PROV Ontology (PROV-O) defines the OWL2 Web Ontology Language encoding of the PROV Data Model [PROV-DM]. This document describes the set of classes, properties, and restrictions that constitute the PROV Ontology. This ontology specification provides the foundation to implement provenance applications in different domains that can represent, exchange, and integrate provenance information generated in different systems and under different contexts. Together with the PROV Access and Query [PROV-AQ] and PROV Data Model [PROV-DM], this document forms a framework for provenance information interchange in domain-specific Web-based applications. 

PROV-O is a lightweight ontology that can be adopted in a wide range of applications. With the exception of five axioms, PROV-O conforms to the OWL-RL profile [OWL2-PRIMER]. The PROV Ontology classes and properties are defined such that they can not only be used directly to represent provenance information, but also can be specialized for modeling application-specific provenance details in a variety of domains. Thus, the PROV Ontology is expected to be both directly usable in applications as well as serve as a reference model for creating domain-specific provenance ontologies and thereby facilitates interoperable provenance modeling. To demonstrate the use of PROV-O classes and properties, this document uses an example provenance scenario similar to the one introduced in the PROV-Primer [PROV-PRIMER]. 

The PROV Data Model [PROV-DM] introduces a set of concepts to represent provenance information in a variety of application domains. This document maps the PROV Data Model to PROV Ontology using the OWL2 ontology language [OWL2-OVERVIEW]. 

We briefly introduce some of the OWL2 modeling terms that will be used to describe the PROV Ontology. An OWL2 instance is an individual object in a domain of discourse, for example a person named Alice or a car named KITT. A set of individuals sharing common characteristics constitutes a class. Person and Car are examples of classes representing the set of individual persons and cars respectively. The OWL2 object properties are used to link individuals, classes, or create a property hierarchy. For example, the object property "hasOwner" can be used to link car with person. The OWL2 datatype properties are used to link individuals or classes to data values, including XML Schema datatypes 

The PROV-O Ontology Description 
The Starting Point category is a small set of classes and properties that can be used to create simple, initial provenance descriptions. Three classes provide a basis for the rest of PROV-O: 

An prov:Entity is a physical, digital, conceptual, or other kind of thing with some fixed aspects; entities may be real or imaginary. An prov:Activity is something that occurs over a period of time and acts upon or with entities; it may include consuming, processing, transforming, modifying, relocating, using, or generating entities. An prov:Agent is something that bears some form of responsibility for an activity taking place, for the existence of an entity, or for another agent's activity. The three primary classes relate to one another and to themselves using the properties shown in the following figure. 

Activities start and end at particular points in time (described using properties prov:startedAtTime and prov:endedAtTime, respectively) and during their lifespan can use and generate a variety of Entities (described with prov:used and prov:wasGeneratedBy, respectively). For example, a blog writing activity may use a particular dataset and generate a bar chart. By expressing usage and generation, one can construct provenance chains comprising both Activities and Entities. 

In addition, we can say that an Activity prov:wasInformedBy another Activity to provide some dependency information without explicitly providing the activities' start and end times. A prov:wasInformedBy relation between Activities suggests that the informed Activity used an Entity that was generated by the informing Activity, but the Entity itself is unknown or is not of interest. So, the prov:wasInformedBy property allows the construction of provenance chains comprising only Activities. 

Provenance chains comprising only Entities can be formed using the prov:wasDerivedFrom property. A derivation is a transformation of one entity into another. For example, if the Activity that created the bar chart is not known or is not of interest, then we can say that the bar chart prov:wasDerivedFrom the dataset. Arbitrary RDF properties can be used to describe the fixed aspects of an Entity that are interesting within a particular application (for example, the file size and format of the dataset, or the aspect ratio of the bar chart). 

While the properties prov:used, prov:wasGeneratedBy, prov:wasInformedBy, and prov:wasDerivedFrom can be used to construct provenance chains among Activities and Entities, Agents may also be ascribed responsibility for any Activity or Entity within a provenance chain. An Agent's responsibility for an Activity or Entity is described using the properties prov:wasAssociatedWith and prov:wasAttributedTo, respectively. Agents can also be responsible for other Agents' actions. In this case of delegation, the influencing Agent prov:actedOnBehalfOf another Agent that also bears responsibility for the influenced Activity or Entity. 

The properties rdf:type and rdfs:label are used to express prov:type and prov:label, respectively.



Read More .....