Ontology Similarity Measuring and Ontology Mapping Algorithms Based on Majorization-Minimization Method

Ontology similarity calculation is important research topics in information retrieval and widely used in science and engineering. In information retrieval, ontology is aiming to find the highly semantic similarity information of the original query concept, and then return the results to the user. Ontology mapping is aiming toconstruct the relationship between two or more ontologies. The core trick of ontology applications is to calculate the similarity between the vertices in the ontology graph. By analyzing the technology of majorization-minimization, we propose the new algorithm for ontology similarity measure and ontology mapping. Via the ontology sparse vector learning, the ontology graph is mapped into a line consists of real numbers. The similarity between two concepts then can be measured by comparing the difference between their corresponding real numbers. The experiment results show that the proposed new algorithm has high accuracy and efficiency on ontology similarity calculation and ontology mapping in biology and physics applications.