International Journal of Metadata, Semantics and Ontologies (4 papers in press)

Regular Issues

• Improving FAIRness of the SYNOP meteorological dataset with semantic metadata  
  by Amina Annane, Mouna Kamel, Cassia Trojahn, Nathalie Aussenac-Gilles, Catherine Comparot, Christophe Baehr 
  Abstract: Meteorological data, essential in a variety of applications, has been made available as open data through different portals, either governmental, associative or private ones. Making this data fully findable and reusable for experts from other domains than meteorology requires considerable efforts to guarantee compliance to the FAIR principles. Nowadays, most efforts in data FAIRification are limited to semantic metadata describing the overall features of datasets. However, such a description is not enough to fully address data interoperability and reusability by other scientific communities. This paper addresses this weakness by proposing a semantic model to represent different kinds of metadata, describing the data schema and the internal structure of a dataset distribution, together with domain-specific definitions. This model is used to provide a reusable schema of the SYNOP dataset, a largely used governmental meteorological dataset in France. The impact of using the proposed model for improving FAIRness was evaluated.
  Keywords: metadata; ontologies; meteorological data; FAIR principles.
  
  
• Nano-PROV: FAIRification workflow for generating nanopublications based on provenance and semantic enrichment  
  by Matheus Pedra Puime Feijoó, Rodrigo Jardim, Sergio Manuel Serra Da Cruz, Maria Luiza Machado Campos 
  Abstract: Providing research data to be readable, accurate and understandable by human and autonomous computational agents is challenging, primarily if published on the Web. We present Nano-PROV, a workflow-based approach that aims to semantic enrichment of data and provenance control of published research datasets. The workflow uses the nanopublications for data transformation, a reliable format for dynamically publishing research outputs. Further, Nano-PROV adopts the UN-PROV, a unified provenance guideline centred on nanopublication for identifying and controlling data and workflow provenance. In this paper, we developed computational experiments to evaluate the workflow by generating a nanopub data model based on the genomic scenario, showing how the proposal may circumvent various issues regarded with data reusability, interoperability, and discoverability issues. Compared with related works, our results demonstrated the feasibility of the Nano-PROV to enhance the semantic expressivity of research data and its metadata annotations.
  Keywords: nanopublication; FAIR principles; FAIRification; data provenance; semantic web; metadata; research data management; ontologies; semantic enrichment.
  
  
• Semantic interoperability model for learning object repositories  
  by Valeria Celeste Sandobal Verón, Mariel Alejandra Ale, Milagros Gutiérrez 
  Abstract: Interoperability among repositories is a crucial issue, which requires not only syntactic but also semantic compatibility, achieved through the adoption of metadata standards. However, different learning object repositories currently use diverse metadata standards to describe their resources, leading to a challenge: multiple metadata standards describe the same term, and the same metadata can describe different terms. To overcome this challenge, this paper proposes an ontology-based interoperability model, featuring a shared vocabulary and a set of matching rules. The shared vocabulary establishes a common terminology for learning objects, while the matching rules enable translation between the shared vocabulary and any metadata standard. As a result, both deposit and search for learning objects can be conducted using any metadata standard, thanks to the rules that ensure seamless translations where needed. To evaluate the proposed model, a prototype has been developed, which implements the shared vocabulary and matching rules. The prototype simulates a system with two repositories, one using the DC metadata standard (for which a DC ontology was used) and the other using LOM (for which a LOM ontology was used).
  Keywords: semantic interoperability; learning object repositories; metadata standards.
  
  
• Enrichment of data in digital documents with metadata extraction  
  by Clovis Santos, Carina Dorneles 
  Abstract: Companies have migrated their operational activities from paper documents to automated processes with fully digital storage. This management trend is positive, but printed documents, in most cases, cannot be discarded for administrative or legal reasons. This research used data extraction to enrich the database of a non-governmental organization (NGO) that monitors the use of public financial resources in counties. The implementation analysed the digital files containing official documents and identified the words with the highest occurrence according to algorithms presented in the research results. The solution created in the research added metadata to improve the search for documents in the database and improve the procedural follow-up of administrative and judicial actions. The results were positive with success in the extraction of the keywords in each document and presented with examples in the results section, showing the steps used to add metadata in the documents.
  Keywords: electronic document; text mining; data extraction; NGO.