Research On Multi-view Commodity Ontology Learning

The development of the Internet and e-commerce putting forward higher requests to the sharing of commodity information and commodity knowledge, a growing number of application research attempt to use ontology to resolve the semantic problem which exists in the process of information exchange. However, most researches are based on an "hypothetical" commodity ontology, the true existing commodity ontologies are not enough, especially the Chinese commodity ontologies. The current Chinese commodity ontologies not only can't meet the certain application scale, but also ignore the multi-view cognitive property when the ontology was designed. These Chinese commodity ontologies cannot describe the commodity knowledge entirely and support most application environments.To solve the problem above, the multi-view commodity ontology should be designed for describing the multi-view cognitive property of commodity and the ontology learning method should be researched for obtaining the anticipant multi-view commodity ontology. Based on the two objects and current research results in the nature language processing, the research work on multi-view commodity ontology modeling and learning are proposed in this paper. The works are described as follows:The hyponymy relations extraction between commodity concepts based on E-catalog. In this part, the paper proposes a hyponymy relations extraction method on the basis of commodity and service catalog in the UNSPSC. The extraction result is the foundation architecture of multi-view commodity ontology which is composed of commodity concepts and category relations between commodity concepts. Besides that, the relation revise algorithm based on phrase construction features given to adjust the extraction result set.The acquisition of commodity attribute based on Web. In this part, the paper brings forward a commodity attribute acquisition strategy which takes the web as the data source. The strategy take different methods according the web page's structuring feature. To the semi-structured page, the strategy uses the acquisition method by the attribute term recognition template and filter template. To the text page, the strategy uses the acquisition method based on Support Vector Machine (SVM) which according to the commodity attribute term's interior and exterior characteristic in the text. In addition to this, a heuristic recognition method based on rules is also proposed to guarantee the accuracy of the SVM acquisition method.The non-category relations learning between commodity concepts based on commodity attributes matching. The learning strategy based on attribute subset matching is proposed and the key technologies of attribute matching method are morphology matching and concepts'similarity calculation. The matching result activates the decision rule of the anonymous relation. The attribute subsets are created by the automatic classification method based on decision tree.Attribute distribution-oriented mining method of commodity subjective knowledge. The paper proposes the mining strategy of attribute's membership degree to certain view type and attribute's association degree to another attribute. The core of this strategy is calculating the distribution of attribute terms in certain text block which belongs to a definite view type. Wherefore, for the unknown content and view type web text, researches on text categorization based on content and based on writing style are necessary. The key process of the attribute's distribution calculation is how to build the calculating models of attribute's membership degree and attribute's association degree.As an application case about multi-view commodity ontology, a semantic integration system based on multi-view commodity ontology is introduced in this part.