Harvesting relational and structured knowledge for ontology building in the WPro architecture

We present two algorithms for supporting semi-automatic ontology building, integrated in WPro, a new architecture for ontology learning from Web documents. The first algorithm automatically extracts ontological entities from tables, by using specific heuristics and WordNet-based analysis. The second algorithm harvests semantic relations from unstructured texts using Natural Language Processing techniques. The integration in WPro allows a friendly interaction with the user for validating and modifying the extracted knowledge, and for uploading it into an existing ontology. Both algorithms show promising performance in the extraction process, and offer a practical means to speed-up the overall ontology building process.