Ontology alignment has been studied for over a decade,and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies....Ontology alignment has been studied for over a decade,and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies.However,very few alignment systems focus on finding complex correspondences.One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships.In this paper,we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark.The data set consists of two ontologies,the GeoLink Base Ontology(GBO)and the GeoLink Modular Ontology(GMO),as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions.The alignment includes 1:1,1:n,and m:n equivalence and subsumption correspondences,and is available in both Expressive and Declarative Ontology Alignment Language(EDOAL)and rule syntax.The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies.This allows it to be used by extensional alignment systems or those that require training data.This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative(OAEI)complex track to help researchers test their automated alignment systems and algorithms.This paper also analyzes the challenges inherent in effectively generating,detecting,and evaluating complex ontology alignments and provides a road map for future work on this topic.展开更多
Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent adv...Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent advantages of metaheuristics algorithm and rarely considers the execution efficiency,especially the multi-objective ontology alignment model.The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications.In this paper,two multi-objective grasshopper optimization algorithms(MOGOA)are proposed to enhance ontology alignment.One isε-dominance concept based GOA(EMO-GOA)and the other is fast Non-dominated Sorting based GOA(NS-MOGOA).The performance of the two methods to align the ontology is evaluated by using the benchmark dataset.The results demonstrate that the proposed EMO-GOA and NSMOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.展开更多
考虑通讯中的通讯效率和通讯时间两个重要指标,给出了具有自动调整移动Agent所携带本体的方法OAMAC(Ontology Alignment Model in Agent Communication)。该方法有两个特点:①Agent通讯实体中包含的概念采用本体方法进行描述,对本体做...考虑通讯中的通讯效率和通讯时间两个重要指标,给出了具有自动调整移动Agent所携带本体的方法OAMAC(Ontology Alignment Model in Agent Communication)。该方法有两个特点:①Agent通讯实体中包含的概念采用本体方法进行描述,对本体做分层松散的社会性管理;②在需要对一个知识领域进行多次通信的情况下,提高了通信的可靠性,减少了网络流量和通信时间。展开更多
文摘Ontology alignment has been studied for over a decade,and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies.However,very few alignment systems focus on finding complex correspondences.One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships.In this paper,we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark.The data set consists of two ontologies,the GeoLink Base Ontology(GBO)and the GeoLink Modular Ontology(GMO),as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions.The alignment includes 1:1,1:n,and m:n equivalence and subsumption correspondences,and is available in both Expressive and Declarative Ontology Alignment Language(EDOAL)and rule syntax.The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies.This allows it to be used by extensional alignment systems or those that require training data.This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative(OAEI)complex track to help researchers test their automated alignment systems and algorithms.This paper also analyzes the challenges inherent in effectively generating,detecting,and evaluating complex ontology alignments and provides a road map for future work on this topic.
基金the Ministry of Education-China Mobile Joint Fund Project(MCM2020J01)。
文摘Ontology alignment is an essential and complex task to integrate heterogeneous ontology.The meta-heuristic algorithm has proven to be an effective method for ontology alignment.However,it only applies the inherent advantages of metaheuristics algorithm and rarely considers the execution efficiency,especially the multi-objective ontology alignment model.The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications.In this paper,two multi-objective grasshopper optimization algorithms(MOGOA)are proposed to enhance ontology alignment.One isε-dominance concept based GOA(EMO-GOA)and the other is fast Non-dominated Sorting based GOA(NS-MOGOA).The performance of the two methods to align the ontology is evaluated by using the benchmark dataset.The results demonstrate that the proposed EMO-GOA and NSMOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.
文摘考虑通讯中的通讯效率和通讯时间两个重要指标,给出了具有自动调整移动Agent所携带本体的方法OAMAC(Ontology Alignment Model in Agent Communication)。该方法有两个特点:①Agent通讯实体中包含的概念采用本体方法进行描述,对本体做分层松散的社会性管理;②在需要对一个知识领域进行多次通信的情况下,提高了通信的可靠性,减少了网络流量和通信时间。
