Ontology evolution from RDF streams using possibilistic axiom scoring

Evolving an ontology involves re-learning, re-enriching and re-validating knowledge in the face of changes to the domain, and techniques applied for them can be adapted to ontology evolution. The possibilistic approach to axiom scoring has been applied to complete and large datasets in ontology learning. This paper presents an adaptation of the possibilistic approach to axiom scoring to the context of RDF data streams for ontology evolution, a scenario which forcefully deals with incomplete and time-dependent data. Possibilistic axiom scoring is used in two distinct scenarios: (1) with previously known property axioms, allowing for the exploration of the effectiveness of the approach in a scenario in which no incorrect data was present; and (2) in an evolving knowledge scenario, in which neither the properties nor the axioms were known and the dataset was obtained from publicly available sources, possibly both incomplete and with errors. Results show the effectiveness of the approach in accepting/rejecting axioms for the ontology’s properties. The different approaches to possibility and necessity proposed in literature are recontextualized in terms of their bias towards examples or counterexamples – showing that some axioms benefit from a more lenient approach, while others present a lower risk of introducing inconsistencies by having harsher acceptance conditions.