This chapter gives an overview over the state-of-the-art in natural language processing for ontology learning. It presents two main NLP techniques for knowledge extraction from text, namely shallow techniques and deep techniques, and explains their usefulness for each step of the ontology learning process. The chapter also advocates the interest of deeper semantic analysis methods for ontology learning. In fact, there have been very few attempts to create ontologies using deep NLP. After a brief introduction to the main semantic analysis approaches, the chapter focuses on lexico-syntactic patterns based on dependency grammars and explains how these patterns can be considered as a step towards deeper semantic analysis. Finally, the chapter addresses the “ontologization” task that is the ability to filter important concepts and relationships among the mass of extracted knowledge.
Wnt pathway curation using automated natural language processing: combining statistical methods with partial and full parse for knowledge extraction