A Study On Web-Based Domain Independent Ontology Learning

Since the Semantic Web had been proposed, ontology is become an important tool in the artificial intelligence and knowledge engineering. And it is of great significance to the acquisition, representation, analysis and application of knowledge areas. An issue named "ontology bottleneck" , the lack of efficient ways to build ontologies, has been coming up to generate ontologies. Therefore, it is an urgent task to improve the methodology for rapid development of more detailed and specialized domain ontologies. A framework of automatic extract ontology knowledge from the existing source of information, which can reduce the cost, is an effective way of ontology rapid-development.At present, the research of Ontology learning is a trend in the computer science dispciline. A lot of ontology learning methods have been proposed, but most of them are not perfect. The existing ontology learning methods are all in need of manual work, and the fully automatic approach is unrealistic in the short term. However, due to the massive nature of Web resources, we still need to further improve the degree of automation, and reduce the participation of users. In addition, most of the ontology learning tools are very limited, because they can only handle certain types of data sources, or capture some objects, but can' t process Chinese corpus. Due to the limitation of the existing ontology learning methods, these tools are still very immature; and some of the latest research results have not been used.In this paper, we combine the NLP and machine learning methods in the open network environment. Firstly, we in-depth discuss the key technologies of ontology learning, and propose a web-based multi-strategy Ontology learning framework (called GOLF). And then we discuss the way of ontology evaluation and evaluate the GOLF by several experiments. The main research contents of the dissertation contains as follows:1) According to layered approach, we propose a layered ontology learning framework, including the automatic extraction of terms, domain concepts learning, instances learning, taxonomy and non-taxonomic relations learning. In order to achieve a seamless integration of ontology learning process, we improve these technologies which are also applied in our ontology learning framework. And originately the ontology evaluation module is integrated in...