Correlative Analysis Method Based On Body-oriented

With the developments of the information technology and mass-storage technology, mass-data in business were stored automatically. It is impossible for human to analysis such huge data and find out the regularity in them manually. So finding out useful knowledge from huge data is an important sign of a modern enterprise informatization development.To process these data and improve the processing efficiency, since the1960’s, the database technology, OLTP, OLAP and Data Warehousing were proposed and applied in different domains rapidly. Based on the data processing, to find out the inherent lows of the huge data, many manual and semi-automatic data-mining technologies were proposed. However, there are great improvement spaces in practical data-mining technologies, which includes background knowledge infuses in data-mining process, the background knowledge guides the mining process and so on. In fact, description of background knowledge is a representation of knowledge of artificial intelligence (AI). Because the domain knowledge ontology presented contains the semantic information the computer can understand, the ontology knowledge representation of AI have enforced the ability of AI knowledge representation greatly and promoted the theory of AI and related technology developed. Traditional data-mining methods can not directly use the background knowledge. To overcome this shortage, we use the ontology and related knowledge representation methods of AI to model fundamental domain knowledge and apply it to commerce data-mining.This research takes the methods of improving the performance correlation analysis method as an entry point, takes the ontology as the key technology, fuses the domain knowledge into data mining to find out the inherent laws of data. In this article, the relevant technology and method of converting the domain knowledge to ontology will be discussed, and each one can understanding the domain knowledge through inquiring the ontology of special field, a method of translating natural language to formal semantic will be presented for maintaining the domain knowledge easily and communicating with people conveniently during data mining process. At last, the terminology and relations defined and the knowledge model constructed in business domain will verify the theory. The contributions of the article present as follows:This paper first studied the main problems in association rules mining and proposed a method of association analysis called OOAA(Ontology Oriented Associate Analysis),which contains ontology knowledge in order to overcome the shortage of application domain knowledge in the process of mining. The OOAA can be used in data prepare, pruning, and description after mining. In comparison to the traditional methods of RA, the OOAA can effectively use background knowledge in mining process which make the mining more controllableWe construct OOAA concept architecture, in which relevant concepts will be described clearly, At the same time, we define and describe the technical framework of the OOAA, and propose the Onto-Apriori algorithm,which can use ontology as background knowledge to guide the related analysis.To solve the problem of translating natural language into formal language, we proposes LTMS method(Language Template Mapping Semantic),which can transform some statements of natural language into an equivalent formal language, such as Chinese in RDF.Prototype System is designed in this research and is verified in commercial field. To implement the prototype system, we analysis the related commercial services in commercial activity, construct the ontology model of commercial data, develop the code of algorithm of Onto-Apriori. The ontology API was used to acquire knowledge, inquire concepts in Ontology model. The related theories are proved through the implement of the algorithms.