Ontology extension based on axiomatic cognitive model for Ontology learning
Dehai Zhang, Zhonghao Yang, Naiyao Wang, Bin Wang, Hang Zhao · 2016
As a kind of knowledge description methods, ontology has been used in reasoning related field to obtain more reasonable results, such as Semantic Web, Decision Support System, Data Integration etc. However, the construction of the ontology is still a time-consuming work for the experts. Although there are many ontology learning methods to acquire ontology from structured or unstructured data, it is still difficult to exclude logic errors using reinforcement learning on ontology construction. Ontology extension is a key step when the new knowledge adds into the existing ontology in the process ontology learning. This proposal designs and validates the method of ontology extension based on the axiomatic cognitive model, which include the ontology extension postulates, axioms and operations of the learning model. Our approach aims at improving the efficiency and accuracy of the Ontology construction. It is proved that these operators subject to the established axiom system.