An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies  

作　　者：Gbede Sylvain Gbame Maho Wielfrid Morie Konan Marcelin Brou Gbede Sylvain Gbame;Maho Wielfrid Morie;Konan Marcelin Brou(Institut National Polytechnique Félix Houphouë,t-Boigny, Yamoussoukro, Cô,te d’Ivoire;Laboratoire de MathématiquesInformatique, Université Nangui Abrogoua, Abidjan, Cô,te d’Ivoire)

机构地区：[1]Institut National Polytechnique Félix Houphouë,t-Boigny, Yamoussoukro, Cô,te d’Ivoire [2]Laboratoire de MathématiquesInformatique, Université Nangui Abrogoua, Abidjan, Cô,te d’Ivoire

出　　处：《Open Journal of Applied Sciences》2023年第11期1896-1909,共14页应用科学（英文）

摘　　要：Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.

关 键 词：ONTOLOGY Data Structure Similarity Measure Concepts Information Content 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]