Ontology evolution continues to be an important problem that needs further research. Key challenges in ontology evolution and creation of a highly consumable ontology include accomodating: (a) the subtle changes in the meaning of a model element over time, (b) the changing relevance of various parts of the model to the user, and (c) the complexity in representing time-varying semantics of model elements in a dynamic domain.

In this work, we address the challenge of evolving an ontology to keep up with the domain changes while focusing on the utility of its content for relevance and imposing constraints for performance. We propose a novel evidence accumulation framework as a principled approach for ontology evolution, which is sufficiently expressive and semantically clear. Our approach classifies model elements (e.g., concepts) into three categories: definitely relevant (that must be included in the ontology), potentially relevant (that can be kept as backup), and irrelevant (that should be removed). Further, our approach dynamically re-classifies models based on external triggers like evidence or internal triggers, like the age of a model in the ontology. As a result, users will have an ontology which is both effective and efficient. We evaluate our approach based on two measures - ontology concept retention and ontology concept placement. This comprehensive evaluation in a single framework is novel and we show that our approach yields promising results.