Ontologies form the core of Semantic Web systems, and as such, they need to evolve to meet the changing needs of the system and its users (e.g. new data or functionalities introduced). Evolving an ontology is a time consuming task, and relies on considerable input from a user with knowledge representation skills. In addition to relying on user input, existing ontology evolution systems do not explore the potential use of background knowledge during the evolution process. Our aim is to reduce the cost of ontology evolution by minimizing user input. We investigate the following sub-problems:

- How to detect the need for change
- How to discover and extract relevant information from external sources
- How to perform dynamically the changes to the ontology
- How to harvest background knowledge to perform the changes
- How to manage the ontology changes

Our hypothesis is that (online) knowledge and data sources (e.g. the Web, Wikipedia, ontologies) could be explored to reduce user input and to realize a dynamic ontology evolution. We propose Evolva, an evolution framework based on this idea, and having the following components: data discovery, data validation, ontological changes, evolution validation and management. For additional details, please check our publications list.