Research And Implementation For Relational Database Oriented Ontology Learning

Ontology learning was to extract semantic from the data model now existing in varies organization, generate new ontology by data reorganization and definition. Centered on the transformation contributions were grouped into the following 3 sections:1, first we introduced the proposal of the Ontology learning concept and its basic idea, and its importance in the semantic web construction. The research and development of transformation with relational database as input and OWL-DL ontology language as output in the Ontology Learning Industry were investigated in depth. Further we compared and analyzed the most popular Ontology languages and their corresponding logic languages.2, Later, To the problem of transform relational data model and data thereby included into a ontological knowledgebase, an algorithm which stems from a method of translating a database model into a conceptual model through Database Inverse Engineering is presented. This algorithm for transformation from Relational Schema to ontology expressed by OWL DL, We proved to be sound.3, The last section is the specifications about the realization and the output results based on the research. We demonstrated the design steps of this experimental system and its significant classes and their functions as well. We also analyzed the output - an OWL-DL ontology file in RDF/XML syntax. As for the methods used for the design of this system, the environment for the system to run, and specifications for the results and evaluation for algorithm we proposed, we also gave specifications in detail.