Semantic Web-based Knowledge And Applied Research

Semantic Web is advanced by Tim Berners-Lee who is the father of the next generation World Wide Web, it is extended from the current Web. The goal of Semantic Web study is strengthening the semantic expression of the Web resources, and expressing information resources in a clear, formal way. So the semantic can express explicitly and the need of semantic interoperability under the distributed mainstream computing environment can satisfy. The key issue of achieving the Semantic Web is semantic expression, the knowledge expression of semantic Web is the base of realizing semantic web completely.This paper has analyzed and studied knowledge expression based on the semantic web. The entire work mainly concentrates on the Semantic Web ontology layer and data layer. It also studied how to use ontology extracting knowledge and modeling knowledge, extracted essential features and attributes of knowledge utilizing ontology and described knowledge representation elements formally. The representation of knowledge representation on Data layers language is RDF(S). This paper explains RDF syntax and semantics of the application based on examples. Along with more and more metadata expressing using RDF form, this paper gives an efficient way of manage RDF data; which is based on the relational database.The study of Ontology layer mainly concentrated on the ontology language OWL and ontology modeling, ontology learning. From the study of ontology language OWL, the paper found series of shortcomings on description language OWL, Such as the restrictive capacity of attributes is inadequate and the expression of rules is weakness. In this paper, Author extended OWL base OWL original grammar, used user-defined operator to change the binding properties; let OWL+ can express rules by the expansion of the predicate.The study of ontology modeling focused on modeling ontology in specific areas. The paper gives a simple examples (ontology conformation on computer accessories) to demonstrate the process of building domain ontology by the use of Protege modeling tool. Aimed at that modeling ontology is laborious and time-consuming, the paper introduces the idea of ontology learning. The aim is to develop a machine learning technology which can automatically modeling ontology to help knowledge works building ontology. In the final chapter, author constructs an ontology learning framework which can automatically create domain ontology by extracting terminologies, creating ontology and pruning ontology.