Ontology Similarity Computation and Ontology Mapping Using Distance Matrix Learning Approach

摘要

Ontology, a common tool in various fields of natural sciences, aims to get the optimal ontology similarity calculation function. Favored by researchers from semantic query and other disciplines, it is used to calculate the similarity between ontology concepts. The semantic information of each concept is expressed by a d-dimensional vector, and the similarity calculation is transformed into the geometric distance calculation of the two corresponding vectors. Using the Mahalanobis distance calculation formula, the ontology algorithm can contribute to getting the optimal distance matrix. In this paper, in terms of the coordinate descent trick and iterative method, we get the ontology distance matrix learning algorithm, and then apply it to ontology similarity computation and ontology mapping. Moreover, the ontology distance matrix learning approach in the manifold setting is discussed, and its kernel solution is studied as well. The main ontology learning algorithm is illustrated by a comparison of other ontology algorithmic data in a specific application context.