Conceptual data model automatic conversion method based on semantic analysis

Abstract

The invention discloses a conceptual data model automatic conversion method based on semantic analysis. The conceptual data model automatic conversion method comprises the steps that (1) words in a conceptual semantic network database and the relation between the words are imported into a Neo4J database; (2) analyzing the relationship between the words in the Neo4J database, and calculating a semantic comparison relationship value between the two words; (3) detecting a concept mapping relationship between a pair of source element models according to the semantic comparison relationship value,correspondingly obtaining entities and attributes in the successfully matched element models, respectively storing the entities and attributes in the unsuccessfully matched element models as new source element models, and repeating the step until all source element models are compared; and (4) matching the new source meta-model with the target meta-model, and taking the successfully matched part as a meta-model conversion rule to obtain an entity and an attribute with potential mapping. According to the method, the model conversion process based on the fine meta-model is defined in the ACDMTM,and a mechanism for generating the conversion rule from the automatically detected potential mapping is defined in the conversion process, so that the conversion efficiency and effectiveness are improved.

Description

technical field [0001] The invention mainly relates to the technical field of information processing, in particular to an automatic conversion method of conceptual data models based on semantic analysis. Background technique [0002] Data are symbols that represent properties of objects, events, and their environments. They are the product of observation. Data arrives from a wide variety of heterogeneous sources at a very rapid rate. Every aspect of society is overwhelmed by the massive amount of generated data. In order to make better use of these data, data integration is necessary. Data integration involves combining data residing in different sources and providing users with a unified view. Data integration plays a key role in generating information and then building a knowledge base. [0003] Science has promoted the progress of entity analysis, but entity analysis not only faces traditional problems such as fuzzy attributes, but also faces more and more data sets and m...

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Problems solved by technology

[0005] Purpose of the invention: The embodiment of the present invention provides an automatic conceptual data model conversion method based on semantic analysis to solve the problems of low reusability, low conversion efficiency and effectiveness in model conversion examples in the prior art

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