Design Of Domain Ontology And Grading Of Document Set In Wetland Protection

The wetland is the richest landscape in zoology variety and one of the most important environment resources for human. It plays an important role in supplying the water resource, meaning the flood, modulating the climate and protecting the variety of life forms. So the wetland is honored as "the kidney of nature". At present, most people are short of wetland protection consciousness, and it is different organization's different understanding and expressing format that badly affects information share and interoperation in wetland. According to systemic research and analyses on each factor of wetland protection through ontology technology, the need drive model on input and output of wetland protection is created, the knowledge set of it is introduced, then the wetland domain ontology is established combining Skeletal Methodology. So, the concepts and relations of wetland can be established definitely, the share and recycle of wetland information can be realized at a certain extent, and then wetland protection recognition of people is consentaneous by step.The conceptive document, relative document and the improved Vector Space Model (VSM) are established, through putting the input and output need drive model and the wetland protection domain ontology into wetland information resources (document set), and combining with the analyses of marked sources location and wetland document set characters as conceptive, relative, and predictive. Then the concept and relation weights are computed, the knowledge and information of wetland document can be quantified. The prediction of document is realized by Neural Network (NN), then the document level can be predicted, and the documents of the set can be graded based on the document existing character.In this paper, after the design of C/S Integrated Framework, Java and Oracle technology are used to establish the document set evaluating system combining with the document set evaluating methods. The system is composed of eight modules, such as initial ontology setting, simulating document production, concept distilling and relation analyzing, and so on. Each module is designed and realized in detail in this paper. Word frequency, marked sources location and ontology relation distances handled by the system are used as input samples of the Back-Propagation Neural Networks (BPNN) to predict the document level.