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
Semantic Web(SW)provides new opportunities for the study and application of big data,massive ranges of data sets in varied formats from multiple sources.Related studies focus on potential SW technologies for resolving...Semantic Web(SW)provides new opportunities for the study and application of big data,massive ranges of data sets in varied formats from multiple sources.Related studies focus on potential SW technologies for resolving big data problems,such as structurally and semantically heterogeneous data that result from the variety of data formats(structured,semi-structured,numeric,unstructured text data,email,video,audio,stock ticker).SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data.In the current research,we implement a new semantic Extract Transform Load(ETL)model that uses SW technologies for aggregating,integrating,and representing data as linked data.First,geospatial data resources are aggregated from the internet,and then a semantic ETL model is used to store the aggregated data in a semantic model after converting it to Resource Description Framework(RDF)format for successful integration and representation.The principal contribution of this research is the synthesis,aggregation,and semantic representation of geospatial data to solve problems.A case study of city data is used to illustrate the semantic ETL model’s functionalities.The results show that the proposed model solves the structural and semantic heterogeneity problems in diverse data sources for successful data aggregation,integration,and representation.展开更多
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.
文摘Semantic Web(SW)provides new opportunities for the study and application of big data,massive ranges of data sets in varied formats from multiple sources.Related studies focus on potential SW technologies for resolving big data problems,such as structurally and semantically heterogeneous data that result from the variety of data formats(structured,semi-structured,numeric,unstructured text data,email,video,audio,stock ticker).SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data.In the current research,we implement a new semantic Extract Transform Load(ETL)model that uses SW technologies for aggregating,integrating,and representing data as linked data.First,geospatial data resources are aggregated from the internet,and then a semantic ETL model is used to store the aggregated data in a semantic model after converting it to Resource Description Framework(RDF)format for successful integration and representation.The principal contribution of this research is the synthesis,aggregation,and semantic representation of geospatial data to solve problems.A case study of city data is used to illustrate the semantic ETL model’s functionalities.The results show that the proposed model solves the structural and semantic heterogeneity problems in diverse data sources for successful data aggregation,integration,and representation.
