Research On Automatic Reasoning Techniques Of Semantic Web In The Context Of Ontology Knowledge Systems

Tim Berners-Lee brought forward the concept of Semantic Web in 1998 and set the goal as to make computer process information automatically by adding metadata to traditional web data to make the latter machine-understandable. The clearer meanings of data and domain theory (Ontology) are supposed to provide high quality services for Web. One major job of making the web data machine-understandable is to let computers understand the connotation of data. Computers' comprehensive capabilities rely mostly on reasoning techniques. So the reasoning techniques play a key role in semantic Web.This thesis firstly analyzed major techniques widely used in semantic web and also ontology theory. It then focused the researches on reasoning techniques in the context of ontology knowledge base systems. The researches included Logic Reasoning and Rule-based Reasoning. On logic reasoning, we brought about a new approach-data type groups to help reason in descriptive logic SHOQ(Dn)and also added tableaux algorithm to SHOQ(Dn). Because of the limitation to the reasoning capabilities of descriptive logic, we tried to combine classical rule-based reasoning method and agent inference engine technique together to help improve the reasoning capabilities of semantic web. At the end of this thesis, we proposed a reasoning prototype system for semantic web and illustrated its implementation.The major contributions of this thesis are as follows:1. brought forward the data type groups approach to help logic reasoning2. improved SHOCQ(Dn) by using Tableaux algorithm for reasoning3. compared network matching algorithm(RETE) with its improved algorithm mathematically, then proposed a method of improving matching efficiency by incorporating multi-agent techniques4. based on Jess reasoning algorithm and agent techniques, we proposed and implemented an effective reasoning prototype system for semantic web.