AQUISIÇÃO AUTOMATIZADA DE HIERARQUIAS DE CONCEITOS DE ONTOLOGIAS UTILIZANDO APRENDIZAGEM ESTATÍSTICA RELACIONAL

Abstract

Knowledge representation formalisms, such as ontologies, have proven to be a powerful tool for enhancing the effectiveness of natural language processing, information filtering and retrieval and so on. Besides these tasks, ontologies are also crucial for the Semantic Web, a new generation of the Web that aims at structuring its content in such a way that it can be more effectively processed by machines. However, knowledge systems suffer from the so called knowledge acquisition bottleneck, i.e. the difficulty in constructing knowledge bases. An approach for this problem is to provide automatic or semi-automatic support for ontology construction, a field of research known as ontology learning. This work discusses the state of the art of ontology learning techniques and proposes and approach for supporting the ontology construction process through the automatization of the concept hierarchy extraction from textual sources. The proposed process is composed by two techniques, namely PRECE (Probabilistic Relational Concept Extraction) and PREHE (Probabilistic Relational Hierarchy Extraction). The PRECE technique extracts ontology concepts from textual sources while the PREHE technique extracts taxonomic relationships between the concepts extracted by PRECE. Both techniques use Markov logic networks, an approach for statistical relational learning that combines first order logic with Markov networks. The PRECE and PREHE techniques were evaluated in the touristic domain and their results were compared with an ontology manually developed by a domain expert.