Approximation analysis of ontology learning algorithm in linear combination setting

Journal of Cloud Computing: Advances, Systems and Applications

卷:9

Yaojun Chen¹  Wei Gao² 

[1] Department of Mathematics, Nanjing University;

[2] School of Information Science and Technology, Yunnan Normal University;

关键词: Ontology;    Learning algorithm;    Multi-dividing setting;    Linear combination;   

DOI  :  10.1186/s13677-020-00173-y

来源: DOAJ

【 摘 要 】

Abstract In the past ten years, researchers have always attached great importance to the application of ontology to its relevant specific fields. At the same time, applying learning algorithms to many ontology algorithms is also a hot topic. For example, ontology learning technology and knowledge are used in the field of semantic retrieval and machine translation. The field of discovery and information systems can also be integrated with ontology learning techniques. Among several ontology learning tricks, multi-dividing ontology learning is the most popular one which proved to be in high efficiency for the similarity calculation of tree structure ontology. In this work, we study the multi-dividing ontology learning algorithm from the mathematical point of view, and an approximation conclusion is presented under the linear representation assumption. The theoretical result obtained here has constructive meaning for the similarity calculation and concrete engineering applications of tree-shaped ontologies. Finally, linear combination multi-dividing ontology learning is applied to university ontologies and mathematical ontologies, and the experimental results imply that the higher efficiency of the proposed approach in actual applications.

【 授权许可】

Unknown