Francisco Delgado Azuara, José Ramón Hilera González and Raul Ruggia
– This report aims to present the state of play of semantic interoperability problems in social security data exchanges.
Abstract
Purpose
This report aims to present the state of play of semantic interoperability problems in social security data exchanges.
Design/methodology/approach
The subject is presented as an open issue and taking into account the circumstances of the exchanges. Electronic exchanges in European Union social security are used as a case study.
Findings
Semantic level of these exchanges seems to be the weakness and the use of controlled vocabularies is proposed as possible solution. The creation and maintenance of metadata sets are finally considered as a compromise solution.
Originality/value
The article proposes a solution for the current semantic problems in electronic exchanges of social security information. The solution could be useful for social security institutions all over the world.
Details
Keywords
Elaheh Hosseini, Kimiya Taghizadeh Milani and Mohammad Shaker Sabetnasab
This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.
Abstract
Purpose
This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.
Design/methodology/approach
This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.
Findings
The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.
Originality/value
This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.