Objectives:The aim of this study was to investigate and develop a data storage and exchange format for the process of automatic systematic reviews(ASR)of traditional Chinese medicine(TCM).Methods:A lightweight and com...Objectives:The aim of this study was to investigate and develop a data storage and exchange format for the process of automatic systematic reviews(ASR)of traditional Chinese medicine(TCM).Methods:A lightweight and commonly used data format,namely,JavaScript Object Notation(JSON),was introduced in this study.We designed a fully described data structure to collect TCM clinical trial information based on the JSON syntax.Results:A smart and powerful data format,JSON-ASR,was developed.JSON-ASR uses a plain-text data format in the form of key/value pairs and consists of six sections and more than 80 preset pairs.JSON-ASR adopts extensible structured arrays to support the situations of multi-groups and multi-outcomes.Conclusion:JSON-ASR has the characteristics of light weight,flexibility,and good scalability,which is suitable for the complex data of clinical evidence.展开更多
Nowadays,ontologies,which are defined under the OWL 2 Web Ontology Language(OWL 2),are being used in several fields like artificial intelligence,knowledge engineering,and Semantic Web environments to access data,answe...Nowadays,ontologies,which are defined under the OWL 2 Web Ontology Language(OWL 2),are being used in several fields like artificial intelligence,knowledge engineering,and Semantic Web environments to access data,answer queries,or infer new knowledge.In particular,ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics.Big data are being widely stored and exchanged in JavaScript Object Notation(JSON)format,in particular by Web applications.However,JSON data collections lack explicit semantics as they are in general schema-less,which does not allow to efficiently leverage the benefits of big data.Furthermore,several applications require bookkeeping of the entire history of big data changes,for which no support is provided by mainstream Big Data management systems,including Not only SQL(NoSQL)database systems.In this paper,we propose an approach,namedJOWL(temporal OWL 2 from temporal JSON),which allows users(i)to automatically build a temporal OWL 2 ontology of data,following the Closed World Assumption(CWA),from temporal JSON-based big data,and(ii)to manage its incremental maintenance accommodating the evolution of these data,in a temporal and multi-schema environment.展开更多
基金the National Key R&D Program of China(Grant no.2019YFC1709803)National Natural Science Foundation of China(Grant no.81873183).
文摘Objectives:The aim of this study was to investigate and develop a data storage and exchange format for the process of automatic systematic reviews(ASR)of traditional Chinese medicine(TCM).Methods:A lightweight and commonly used data format,namely,JavaScript Object Notation(JSON),was introduced in this study.We designed a fully described data structure to collect TCM clinical trial information based on the JSON syntax.Results:A smart and powerful data format,JSON-ASR,was developed.JSON-ASR uses a plain-text data format in the form of key/value pairs and consists of six sections and more than 80 preset pairs.JSON-ASR adopts extensible structured arrays to support the situations of multi-groups and multi-outcomes.Conclusion:JSON-ASR has the characteristics of light weight,flexibility,and good scalability,which is suitable for the complex data of clinical evidence.
文摘Nowadays,ontologies,which are defined under the OWL 2 Web Ontology Language(OWL 2),are being used in several fields like artificial intelligence,knowledge engineering,and Semantic Web environments to access data,answer queries,or infer new knowledge.In particular,ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics.Big data are being widely stored and exchanged in JavaScript Object Notation(JSON)format,in particular by Web applications.However,JSON data collections lack explicit semantics as they are in general schema-less,which does not allow to efficiently leverage the benefits of big data.Furthermore,several applications require bookkeeping of the entire history of big data changes,for which no support is provided by mainstream Big Data management systems,including Not only SQL(NoSQL)database systems.In this paper,we propose an approach,namedJOWL(temporal OWL 2 from temporal JSON),which allows users(i)to automatically build a temporal OWL 2 ontology of data,following the Closed World Assumption(CWA),from temporal JSON-based big data,and(ii)to manage its incremental maintenance accommodating the evolution of these data,in a temporal and multi-schema environment.
