The Information Technology (IT) developments have changed the use of Healthcare terminologies from paper-based mortality statistics with the WHO international classifications of diseases (ICD) to the IT-based morbidit...The Information Technology (IT) developments have changed the use of Healthcare terminologies from paper-based mortality statistics with the WHO international classifications of diseases (ICD) to the IT-based morbidity implementations for instance for Casemix-based healthcare funding and managing systems. This higher level of granularity is worldwide spread under the umbrella of several national modifications named ICD10 XM. These developments have met the increased use of the International Clinical Reference Terminology named SNOMED. When the updating of WHO ICD10 to WHO ICD11 was decided a merging was envisaged and a WHO SNOMED CT common work proposed a methodology to create a common formal ontology between the 11th version of the WHO International Classification of Diseases and Health Problems (ICD) and the most used in the world clinical terminology named Systematized Nomenclature of Human and Veterinary Medicine - Clinical Terms (SCT). The present work follows this unachieved work and aims to develop a SNOMED-based formal ontology for ICD11 chapter 1 using the textual definitions of ICD11 codes which is a completely new character of ICD and the ontology tools provided by SCT in the publicly available SNOMED Browser. There are two key results: the lexical alignment is complete and the ontology alignment is incomplete with the validated SNOMED concept model can be completed with not yet validated attributes and values of the SNOMED Compositional Grammar. The work opens a new era for the seamless use of both international terminologies for morbidity for instance for DRG/Casemix and clinical management use. The main limitation is that it is restricted to 1 out of 26 chapters of ICD11.展开更多
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
文摘The Information Technology (IT) developments have changed the use of Healthcare terminologies from paper-based mortality statistics with the WHO international classifications of diseases (ICD) to the IT-based morbidity implementations for instance for Casemix-based healthcare funding and managing systems. This higher level of granularity is worldwide spread under the umbrella of several national modifications named ICD10 XM. These developments have met the increased use of the International Clinical Reference Terminology named SNOMED. When the updating of WHO ICD10 to WHO ICD11 was decided a merging was envisaged and a WHO SNOMED CT common work proposed a methodology to create a common formal ontology between the 11th version of the WHO International Classification of Diseases and Health Problems (ICD) and the most used in the world clinical terminology named Systematized Nomenclature of Human and Veterinary Medicine - Clinical Terms (SCT). The present work follows this unachieved work and aims to develop a SNOMED-based formal ontology for ICD11 chapter 1 using the textual definitions of ICD11 codes which is a completely new character of ICD and the ontology tools provided by SCT in the publicly available SNOMED Browser. There are two key results: the lexical alignment is complete and the ontology alignment is incomplete with the validated SNOMED concept model can be completed with not yet validated attributes and values of the SNOMED Compositional Grammar. The work opens a new era for the seamless use of both international terminologies for morbidity for instance for DRG/Casemix and clinical management use. The main limitation is that it is restricted to 1 out of 26 chapters of ICD11.
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.
