针对现有业务规则建模标准SBVR(Semantics of Business Vocabulary and Business Rules)主要面向业务专家,无法被计算机系统直接理解的问题,基于最新的SBVR 2019标准,提出了一种SBVR向Web本体语言OWL2(Web Ontology Language)的转换方...针对现有业务规则建模标准SBVR(Semantics of Business Vocabulary and Business Rules)主要面向业务专家,无法被计算机系统直接理解的问题,基于最新的SBVR 2019标准,提出了一种SBVR向Web本体语言OWL2(Web Ontology Language)的转换方法。首先,通过分析SBVR和OWL2的结构差异,设计了相应的映射规则和转换算法;其次,开发了一个SBVR到OWL2的在线转换系统,以标准化、可扩展的方式实现了业务流程的语义化;最后,通过石油领域的业务流程案例验证了该方法的可行性和实用性,证明了其在促进企业数字化转型中的应用潜力,并能为企业在业务流程的语义化和跨系统的知识共享方面提供有效的技术解决方案。展开更多
With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms o...With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task.At present,the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently.Therefore,we propose an Ontology Matching Method Based on the Gated Graph Attention Model(OM-GGAT).Firstly,the semantic knowledge related to concepts in the ontology is encoded into vectors using the OWL2Vec^(*)method,and the relevant path information from the root node to the concept is embedded to understand better the true meaning of the concept itself and the relationship between concepts.Secondly,the ontology is transformed into the corresponding graph structure according to the semantic relation.Then,when extracting the features of the ontology graph nodes,different attention weights are assigned to each adjacent node of the central concept with the help of the attention mechanism idea.Finally,gated networks are designed to further fuse semantic and structural embedding representations efficiently.To verify the effectiveness of the proposed method,comparative experiments on matching tasks were carried out on public datasets.The results show that the OM-GGAT model can effectively improve the efficiency of ontology matching.展开更多
Context and Objective: Over the past few decades, terminologies developed for clinical descriptions have been increasingly used as key resources for knowledge management, data integration, and decision support to the ...Context and Objective: Over the past few decades, terminologies developed for clinical descriptions have been increasingly used as key resources for knowledge management, data integration, and decision support to the extent that today they have become essential in the biomedical and health field. Among these clinical terminologies, some may possess the characteristics of one or several types of representation. This is the case for the Systematized Nomenclature of Human and Veterinary Medicine—Clinical Terms (SNOMED CT), which is both a clinical medical terminology and a formal ontology based on the principles of semantic web. Methods: We present and discuss, on one hand, the compliance of SNOMED CT with the requirements of a reference clinical terminology and, on the other hand, the specifications of the features and constructions of descriptive of SNOMED CT. Results: We demonstrate the consistency of the reference clinical terminology SNOMED CT with the principles stated in James J. Cimino’s desiderata and we also show that SNOMED CT contains an ontology based on the EL profile of OWL2 with some simplifications. Conclusions: The duality of SNOMED CT shown is crucial for understanding the versatility, depth, and scope in the health field.展开更多
Temporal ontologies allow to represent not only concepts,their properties,and their relationships,but also time-varying information through explicit versioning of definitions or through the four-dimensional perduranti...Temporal ontologies allow to represent not only concepts,their properties,and their relationships,but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view.They are widely used to formally represent temporal data semantics in several applications belonging to different fields(e.g.,Semantic Web,expert systems,knowledge bases,big data,and artificial intelligence).They facilitate temporal knowledge representation and discovery,with the support of temporal data querying and reasoning.However,there is no standard or consensual temporal ontology query language.In a previous work,we have proposed an approach namedτJOWL(temporal OWL 2 from temporal JSON,where OWL 2 stands for"OWL 2 Web Ontology Language"and JSON stands for"JavaScript Object Notation").τJOWL allows(1)to automatically build a temporal OWL 2 ontology of data,following the Closed World Assumption(CWA),from temporal JSON-based big data,and(2)to manage its incremental maintenance accommodating their evolution,in a temporal and multi-schema-version environment.In this paper,we propose a temporal ontology query language forτJOWL,namedτSQWRL(temporal SQWRL),designed as a temporal extension of the ontology query language—Semantic Query-enhanced Web Rule Language(SQWRL).The new language has been inspired by the features of the consensual temporal query language TSQL2(Temporal SQL2),well known in the temporal(relational)database community.The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any(complex)temporal query on time-varying ontologies generated from time-varying big data.Some examples,in the Internet of Healthcare Things(IoHT)domain,are provided to motivate and illustrate our proposal.展开更多
文摘针对现有业务规则建模标准SBVR(Semantics of Business Vocabulary and Business Rules)主要面向业务专家,无法被计算机系统直接理解的问题,基于最新的SBVR 2019标准,提出了一种SBVR向Web本体语言OWL2(Web Ontology Language)的转换方法。首先,通过分析SBVR和OWL2的结构差异,设计了相应的映射规则和转换算法;其次,开发了一个SBVR到OWL2的在线转换系统,以标准化、可扩展的方式实现了业务流程的语义化;最后,通过石油领域的业务流程案例验证了该方法的可行性和实用性,证明了其在促进企业数字化转型中的应用潜力,并能为企业在业务流程的语义化和跨系统的知识共享方面提供有效的技术解决方案。
基金supported by the National Natural Science Foundation of China(grant numbers 62267005 and 42365008)the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing.
文摘With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task.At present,the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently.Therefore,we propose an Ontology Matching Method Based on the Gated Graph Attention Model(OM-GGAT).Firstly,the semantic knowledge related to concepts in the ontology is encoded into vectors using the OWL2Vec^(*)method,and the relevant path information from the root node to the concept is embedded to understand better the true meaning of the concept itself and the relationship between concepts.Secondly,the ontology is transformed into the corresponding graph structure according to the semantic relation.Then,when extracting the features of the ontology graph nodes,different attention weights are assigned to each adjacent node of the central concept with the help of the attention mechanism idea.Finally,gated networks are designed to further fuse semantic and structural embedding representations efficiently.To verify the effectiveness of the proposed method,comparative experiments on matching tasks were carried out on public datasets.The results show that the OM-GGAT model can effectively improve the efficiency of ontology matching.
文摘Context and Objective: Over the past few decades, terminologies developed for clinical descriptions have been increasingly used as key resources for knowledge management, data integration, and decision support to the extent that today they have become essential in the biomedical and health field. Among these clinical terminologies, some may possess the characteristics of one or several types of representation. This is the case for the Systematized Nomenclature of Human and Veterinary Medicine—Clinical Terms (SNOMED CT), which is both a clinical medical terminology and a formal ontology based on the principles of semantic web. Methods: We present and discuss, on one hand, the compliance of SNOMED CT with the requirements of a reference clinical terminology and, on the other hand, the specifications of the features and constructions of descriptive of SNOMED CT. Results: We demonstrate the consistency of the reference clinical terminology SNOMED CT with the principles stated in James J. Cimino’s desiderata and we also show that SNOMED CT contains an ontology based on the EL profile of OWL2 with some simplifications. Conclusions: The duality of SNOMED CT shown is crucial for understanding the versatility, depth, and scope in the health field.
文摘Temporal ontologies allow to represent not only concepts,their properties,and their relationships,but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view.They are widely used to formally represent temporal data semantics in several applications belonging to different fields(e.g.,Semantic Web,expert systems,knowledge bases,big data,and artificial intelligence).They facilitate temporal knowledge representation and discovery,with the support of temporal data querying and reasoning.However,there is no standard or consensual temporal ontology query language.In a previous work,we have proposed an approach namedτJOWL(temporal OWL 2 from temporal JSON,where OWL 2 stands for"OWL 2 Web Ontology Language"and JSON stands for"JavaScript Object Notation").τJOWL allows(1)to automatically build a temporal OWL 2 ontology of data,following the Closed World Assumption(CWA),from temporal JSON-based big data,and(2)to manage its incremental maintenance accommodating their evolution,in a temporal and multi-schema-version environment.In this paper,we propose a temporal ontology query language forτJOWL,namedτSQWRL(temporal SQWRL),designed as a temporal extension of the ontology query language—Semantic Query-enhanced Web Rule Language(SQWRL).The new language has been inspired by the features of the consensual temporal query language TSQL2(Temporal SQL2),well known in the temporal(relational)database community.The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any(complex)temporal query on time-varying ontologies generated from time-varying big data.Some examples,in the Internet of Healthcare Things(IoHT)domain,are provided to motivate and illustrate our proposal.
