[目的/意义]评价Linked Open Data Enabled Bibliographical Data(LODE-BD)3.0一书在开放关联数据赋能书目数据方面做出的学术贡献,帮助读者掌握开放关联数据的应用技能。[方法/过程]阐述开放关联数据应用指南的编撰目的,理解LODE-BD的...[目的/意义]评价Linked Open Data Enabled Bibliographical Data(LODE-BD)3.0一书在开放关联数据赋能书目数据方面做出的学术贡献,帮助读者掌握开放关联数据的应用技能。[方法/过程]阐述开放关联数据应用指南的编撰目的,理解LODE-BD的实践建议,思考如何将书目数据表示为开放关联数据,帮助用户开放获取相关的书目资源,实现书目资源的互联互通。[结果/结论]该书是一本成熟的,关于如何选择合适编码策略来生成开放关联数据赋能的书目数据的操作指南,具有丰富的理论价值、方法指导与实践意义。展开更多
Purpose:To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the f...Purpose:To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the functions and usages of LOD KOS products.Design/methodology/approach:Data collection and analysis were conducted at three time periods in 2015–16,2017 and 2019.The sample data used in the comprehensive data analysis comprises all datasets tagged as types of KOS in the Datahub and extracted through their respective SPARQL endpoints.A comparative study of the LOD KOS collected from terminology services Linked Open Vocabularies(LOV)and BioPortal was also performed.Findings:The study proposes a set of Functional,Impactful and Transformable(FIT)metrics for LOD KOS as value vocabularies.The FAIR principles,with additional recommendations,are presented for LOD KOS as open data.Research limitations:The metrics need to be further tested and aligned with the best practices and international standards of both open data and various types of KOS.Practical implications:Assessment performed with FAIR and FIT metrics support the creation and delivery of user-friendly,discoverable and interoperable LOD KOS datasets which can be used for innovative applications,act as a knowledge base,become a foundation of semantic analysis and entity extractions and enhance research in science and the humanities.Originality/value:Our research provides best practice guidelines for LOD KOS as value vocabularies.展开更多
Standards to describe soil properties are well established,with many ISO specifications and a few international thesauri available for specific applications.Besides,in recent years,the European directive on "Infr...Standards to describe soil properties are well established,with many ISO specifications and a few international thesauri available for specific applications.Besides,in recent years,the European directive on "Infrastructure for Spatial Information in the European Community(INSPIRE)"has brought together most of the existing standards into a well defined model.However,the adoption of these standards so far has not reached the level of semantic interoperability,defined in the paper,which would facilitate the building of data services that reuse and combine data from different sources.This paper reviews standards for describing soil data and reports on the work done within the EC funded agINFRA project to apply Linked Data technologies to existing standards and data in order to improve the interoperability of soil datasets.The main result of this work is twofold.First,an RDF vocabulary for soil concepts based on the UML INSPIRE model was published.Second,a KOS(Knowledge Organization System)for soil data was published and mapped to existing relevant KOS,based on the analysis of the SISI database of the CREA of Italy.This work also has a methodological value,in that it proposes and applies a methodology to standardize metadata used in local scientific databases,a very common situation in the scientific domain.Finally,this work aims at contributing towards a wider adoption of the INSPIRE directive,by providing an RDF version of it.展开更多
Heterogeneous data,different definitions and incompatible models are a huge problem in many domains,with no exception for the field of energy systems analysis.Hence,it is hard to re-use results,compare model results o...Heterogeneous data,different definitions and incompatible models are a huge problem in many domains,with no exception for the field of energy systems analysis.Hence,it is hard to re-use results,compare model results or couple models at all.Ontologies provide a precisely defined vocabulary to build a common and shared conceptu-alisation of the energy domain.Here,we present the Open Energy Ontology(OEO)developed for the domain of energy systems analysis.Using the OEO provides several benefits for the community.First,it enables consistent annotation of large amounts of data from various research projects.One example is the Open Energy Platform(OEP).Adding such annotations makes data semantically searchable,exchangeable,re-usable and interoperable.Second,computational model coupling becomes much easier.The advantages of using an ontology such as the OEO are demonstrated with three use cases:data representation,data annotation and interface homogenisation.We also describe how the ontology can be used for linked open data(LOD).展开更多
Linked data is a decentralized space of interlinked Resource Description Framework(RDF) graphs that are published,accessed,and manipulated by a multitude of Web agents.Here,we present a multi-agent framework for minin...Linked data is a decentralized space of interlinked Resource Description Framework(RDF) graphs that are published,accessed,and manipulated by a multitude of Web agents.Here,we present a multi-agent framework for mining hypothetical semantic relations from linked data,in which the discovery,management,and validation of relations can be carried out independently by different agents.These agents collaborate in relation mining by publishing and exchanging inter-dependent knowledge elements,e.g.,hypotheses,evidence,and proofs,giving rise to an evidentiary network that connects and ranks diverse knowledge elements.Simulation results show that the framework is scalable in a multi-agent environment.Real-world applications show that the framework is suitable for interdisciplinary and collaborative relation discovery tasks in social domains.展开更多
文摘[目的/意义]评价Linked Open Data Enabled Bibliographical Data(LODE-BD)3.0一书在开放关联数据赋能书目数据方面做出的学术贡献,帮助读者掌握开放关联数据的应用技能。[方法/过程]阐述开放关联数据应用指南的编撰目的,理解LODE-BD的实践建议,思考如何将书目数据表示为开放关联数据,帮助用户开放获取相关的书目资源,实现书目资源的互联互通。[结果/结论]该书是一本成熟的,关于如何选择合适编码策略来生成开放关联数据赋能的书目数据的操作指南,具有丰富的理论价值、方法指导与实践意义。
基金College of Communication and Information(CCI)Research and Creative Activity Fund,Kent State University
文摘Purpose:To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the functions and usages of LOD KOS products.Design/methodology/approach:Data collection and analysis were conducted at three time periods in 2015–16,2017 and 2019.The sample data used in the comprehensive data analysis comprises all datasets tagged as types of KOS in the Datahub and extracted through their respective SPARQL endpoints.A comparative study of the LOD KOS collected from terminology services Linked Open Vocabularies(LOV)and BioPortal was also performed.Findings:The study proposes a set of Functional,Impactful and Transformable(FIT)metrics for LOD KOS as value vocabularies.The FAIR principles,with additional recommendations,are presented for LOD KOS as open data.Research limitations:The metrics need to be further tested and aligned with the best practices and international standards of both open data and various types of KOS.Practical implications:Assessment performed with FAIR and FIT metrics support the creation and delivery of user-friendly,discoverable and interoperable LOD KOS datasets which can be used for innovative applications,act as a knowledge base,become a foundation of semantic analysis and entity extractions and enhance research in science and the humanities.Originality/value:Our research provides best practice guidelines for LOD KOS as value vocabularies.
基金The research leading to these results has received funding from the European Union Seventh Framework Programme(FP7/2007-2013)under grant agreement No.283770.
文摘Standards to describe soil properties are well established,with many ISO specifications and a few international thesauri available for specific applications.Besides,in recent years,the European directive on "Infrastructure for Spatial Information in the European Community(INSPIRE)"has brought together most of the existing standards into a well defined model.However,the adoption of these standards so far has not reached the level of semantic interoperability,defined in the paper,which would facilitate the building of data services that reuse and combine data from different sources.This paper reviews standards for describing soil data and reports on the work done within the EC funded agINFRA project to apply Linked Data technologies to existing standards and data in order to improve the interoperability of soil datasets.The main result of this work is twofold.First,an RDF vocabulary for soil concepts based on the UML INSPIRE model was published.Second,a KOS(Knowledge Organization System)for soil data was published and mapped to existing relevant KOS,based on the analysis of the SISI database of the CREA of Italy.This work also has a methodological value,in that it proposes and applies a methodology to standardize metadata used in local scientific databases,a very common situation in the scientific domain.Finally,this work aims at contributing towards a wider adoption of the INSPIRE directive,by providing an RDF version of it.
基金This work was supported by grants from the Federal Ministry for Economic Affairs and Energy of Germany(BMWi)for the projects SzenarienDB(03ET4057A-D),LOD-GEOSS(03EI1005A-G)and SIROP(03EI1035A-D).
文摘Heterogeneous data,different definitions and incompatible models are a huge problem in many domains,with no exception for the field of energy systems analysis.Hence,it is hard to re-use results,compare model results or couple models at all.Ontologies provide a precisely defined vocabulary to build a common and shared conceptu-alisation of the energy domain.Here,we present the Open Energy Ontology(OEO)developed for the domain of energy systems analysis.Using the OEO provides several benefits for the community.First,it enables consistent annotation of large amounts of data from various research projects.One example is the Open Energy Platform(OEP).Adding such annotations makes data semantically searchable,exchangeable,re-usable and interoperable.Second,computational model coupling becomes much easier.The advantages of using an ontology such as the OEO are demonstrated with three use cases:data representation,data annotation and interface homogenisation.We also describe how the ontology can be used for linked open data(LOD).
基金supported by the National Natural Science Foundation of China (Nos.61070156 and 61100183)the Natural Science Foundation of Zhejiang Province,China (No.Y1110477)
文摘Linked data is a decentralized space of interlinked Resource Description Framework(RDF) graphs that are published,accessed,and manipulated by a multitude of Web agents.Here,we present a multi-agent framework for mining hypothetical semantic relations from linked data,in which the discovery,management,and validation of relations can be carried out independently by different agents.These agents collaborate in relation mining by publishing and exchanging inter-dependent knowledge elements,e.g.,hypotheses,evidence,and proofs,giving rise to an evidentiary network that connects and ranks diverse knowledge elements.Simulation results show that the framework is scalable in a multi-agent environment.Real-world applications show that the framework is suitable for interdisciplinary and collaborative relation discovery tasks in social domains.
