The Research Of Ontology Establishing Technology Base On FCA And Statistics

Since the 1990s, the development of information science is facing many new problems, mainly, such as knowledge representation, information organization, and software utilization. Particularly because of the rapid development of the Internet, how to organize, manage and maintain a amount of information and provide users with efficient service has become an important and urgent research content. To meet these requirements, ontology, a concept from the field of philosophy, is introduced to the field of information science, due to a number of researches, it has become a concept modeling tools which can describe information systems on the semantic and knowledge levels, and has been widely used in information retrieval, information extraction, heterogeneous information systems interoperability and integration, Semantic Web and other applications.This paper mainly focuses on generating technologies of ontology construction and ontology optimization method. Paper represents a detailed analysis of the current classical ontology generation methods, with analyzing their strengths and weakness, paper gives the ontology generation algorithms base on the combination of the proposed FCA and statistical learning. Proposes four statistics in the construction of concept lattice and construct mapping process from concept lattice to ontology. In addition, base on the existing theory of FCA concept lattice, in particular, inheritance concept lattice theory, paper draws inferences about the concept of redundancy and the definition of optimizing concept lattice, thus forming the basis of ontology optimization algorithm, and gives a research of similar ontology conducting. Through the above process Ontology, a more ideal ontology structure can be got with ontology generation and optimization. Finally, this paper gives a framework of generating ontology and generates the interface for visualization of ontology construction.Through the ontology constructing techniques and methods of this paper, which can accurately extract the domain knowledge, and establish a clear and focused domain knowledge ontology, which has great significance to effectively organize, manage, maintain a large amount of information and extract critical knowledge from a large number of information.