Forthcoming and Online First Articles

International Journal of Metadata, Semantics and Ontologies

International Journal of Metadata, Semantics and Ontologies (IJMSO)

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International Journal of Metadata, Semantics and Ontologies (3 papers in press)

Regular Issues

  •   Free full-text access Open AccessO'FAIRe makes you an offer: Metadata-based Automatic FAIRness Assessment for Ontologies and Semantic Resources
    ( Free Full-text Access ) CC-BY-NC-ND
    by Emna Amdouni, Syphax Bouazzouni, Clement Jonquet 
    Abstract: FAIRness assessment evaluates the degree to which a digital object is Findable, Accessible, Interoperable, and Reusable. Here, our objects of interest are semantic resources (from thesauri, terminologies, vocabularies to ontologies). Indeed, we have not yet seen a clear methodology implemented and tooled to automatically assess the level of FAIRness of semantic resources. The main objective of this work is to provide such methodology and tooling to guide semantic stakeholders for: (i) making their semantic resources FAIR through better use of standardised metadata; (ii) selecting relevant FAIR semantic resources for use. We propose a metadata-based automatic FAIRness assessment methodology for ontologies and semantic resources called Ontology FAIRness Evaluator (OFAIRe). It is based on the projection of the 15 foundational FAIR principles for ontologies, and it is aligned and nourished with relevant state-of-the-art initiatives for FAIRness assessment. We propose 61 questions of which 80% are based on the resource metadata descriptions, and we review the standard metadata properties (taken from the MOD 1.4 ontology metadata model) that could be used to implement these metadata descriptions and improve the level of FAIRness of any semantic resource. We also demonstrate the importance of relying on ontology libraries or repositories to harmonise and harness unified metadata and thus allow FAIRness assessment. Moreover, we have implemented OFAIRe in the AgroPortal semantic resource repository and produced a preliminary FAIRness analysis over 149 semantic resources in the agri-food/environment domain.
    Keywords: automatic FAIR assessment; semantic web; ontologies; standardised metadata and open repositories.

  • Formalising contextual expert knowledge for causal discovery in linked knowledge graphs about transformation processes: application to processing of bio-composites for food packaging   Order a copy of this article
    by Melanie Munch, Patrice Buche, Cristina Manfredotti, Pierre-Henri Wuillemin, Helene Angellier-Coussy 
    Abstract: With numerous parameters and criteria to take into account, transformation processes are a challenge to model and reason about. This work can be eased thanks to knowledge graphs, which are a widespread practice for formalising knowledge associated with structured and specialised vocabulary about a given domain. They allow to draw semantic relations between concepts, and thus offer numerous tools for reasoning over complex queries. Yet, some of these queries in transformation processes might rely on an additional layer hard to transcribe: uncertainty. In this article, we present how knowledge graphs and probabilistic models can benefit each other for reasoning over transformation processes, and address the necessity of formalising contextual expert knowledge for this combination. We then show how this can be used for (1) reverse engineering approaches and (2) linking knowledge bases, through a detailed example on the process of biocomposites for food packaging.
    Keywords: knowledge graph; probabilistic model; expert knowledge; causality; linked open data.

  • Semantic association rules for data interestingness using domain ontology   Order a copy of this article
    by Abhilash C B, Kavi Mahesh 
    Abstract: The COVID-19 pandemic is a major public health crisis threatening peoples health, well-being, freedom to travel, and the global economy. Scientists and researchers worldwide are developing vaccines and precautions that need to be adopted to control the pandemic. However, there are still concerns about the vaccines efficacy and generalisability. Therefore, understanding COVID-19 symptoms for determining the severity of cases is critical. This study aimed to discover interesting facts from the COVID-19 dataset considering symptoms, medicines, and comorbidity conditions among COVID-19 patients. For data mining research, the semantic web gives up new possibilities. The RDF triple format is commonly used to express semantic web data (subject, predicate, object). Hundreds of millions of resource description framework (RDF) triples represent knowledge in a machine-understandable format in large RDF-style knowledge bases. Association rule mining is one of the most effective methods of detecting frequent patterns.
    Keywords: semantic rules; ontology-based techniques; COVID-19; data interestingness; association rule mining; knowledge discovery.