The Research Of Type-2 Fuzzy Description Logics And Its Application In Ontology Evolution

The available resource in WWW increased greatly due to the high-speed development of network and the exploding of infomarion. There are many expert system expect to build an enormous knowledge base with these resources. Most of these resources are available, however, they are hard to be understood by computers. How to integrate these resources and make computers understand the semantic element form them is becoming a new challenge for artificial intelligence. Ontology is proposed to handle the problem mentioned above. Supported by ontology, knowledge from different domain can be integrate together conveniently through knowledge representation and understood by different computers.However, since the describing ability of Description Logics (DLs), the standard supporting logics for ontology, is strong enough, DLs cannot represent or infer the imprecise information around us. Which makes the ontology based on DLs can hardly support the fuzzy expert system. At the same time, Business dynamics and changes in the operating environment often give rise to continuous changes in application requirements that may be fulfilled only by changing the underlying ontologies.There are many researchs toward the type-1 fuzzy extended version of DLs, which describe the imprecise information by a crisp value. Type-1 fuzzy DL cannot accommodate itself to the real-world well and has many limits for knowledge representation. A type-2 fuzzy DL is proposed for this problem. We discuss the syntax and semantic of type-2 fuzzy DL, which is expected to be applied into fuzzy expert system. At last, this fuzzy DL has been implemented in the semantic search engine system to analyse its functions.Ontology Web Language (OWL), the standard language for Ontology modeling, can express the concepts in the real world and the relationship between them. However, OWL can describe the relationship for inference between concepts. Semantic Web Rule Language (SWRL) is introduced to describe the reasoning rules of ontology concepts. That means a whole ontology knowledge base needs to be able to support the reasoning based on rules. With the extend of Tableau algorithm, a complete set of inference flow for type-2 fuzzy SWRL ontology is proposed based on the type-2 fuzzy reasoning technique.For the reason of most information is stored in relational database, an Architecture of ontology evolution is designed based on data mining to build a fuzzy ontology evolution system which can handle fuzzy ontology with large volume data. A new clustering algorithm named SCT is raised, which makes system be more suitable to the category data in data source of ontology. SCT can classify the records in table of relational database automatically, and generate a hiberarchy of concepts. In order to extract information from relational database to ontology, a set of switch rules is proposed. At last , a fuzzy ontology evolution system named"Grampus"is implemented based on architecture introduced above. Its main functions including user-oriented structure-driven change discovery and implementation, data-driven change discovery and implementation based on data mining, ontology data source extraction based on data mining and rules, original ontology building orienting relational database. The above theoretical principles and practical techniques are adopt for developing a prototype. The experiments are carried out to report the evaluation of arithmetics and results of performance analysis.