Building Ontologies from Textual Resources: A Pattern Based Improvement Using Deep Linguistic Information

Ontologies are a key component for several applications. Ontologies are often built by hand, but automatizing the process of ontology building has been and is even more recognized as very important for scaling and speeding up this process. However, several difficulties have been identified, some of them are quite fundamental. In this paper, we present our work for overcoming some of the fundamental difficulties. Our work resulted in improvements of an existing ontology building tool (Text2Onto). The contribution of our work consists in the creation of a flexible language (DTPL—Dependency Tree Patterns Language) for expressing patterns as syntactic dependency trees to extract semantic relations, and making an existing ontology building tool (Text2Onto) able to use them. DTPL allows to exploit deep linguistic information (related to coreference resolutions, conjunctions, appositions, passive verbal phrases, etc.) provided by deep syntactic analysis of the text, and also (in order to improve the accuracy of patterns) to express the exclusion of some dependency bindings in patterns.