International Journal of Metadata, Semantics and Ontologies (8 papers in press)

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  O'FAIRe makes you an offer: metadata-based automatic FAIRness assessment for ontologies and semantic resources ( Free Full-text Access )

  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.
  DOI: 10.1504/IJMSO.2022.10054778
   
  
• Formalising contextual expert knowledge for causal discovery in linked knowledge graphs about transformation processes: application to processing of bio-composites for food packaging  
  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  
  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.
  DOI: 10.1504/IJMSO.2023.10055590
   
  
• Semantic structure for describing IoT system: application for smart home and smart airport  
  by Achour Fatma, Chaima Bejaoui, Anis Jedid 
  Abstract: The Internet of Things (IoT) is the interconnection between the internet and objects, places, and physical environments. These objects can exchange information and digital data between the real world and the internet. The ubiquitous connectivity provided by the IoT requires integration between heterogeneous sensors, actuators, mobile phones, etc. Extending the IoT with web services technology is a means to achieve interoperable communications between objects and makes it easy to integrate dynamic distributed processes. Accordingly, a phase of distant object identification is mandatory to ensure the association of semantics with these heterogeneous data through ontologies to not only facilitate their reuse but also to allow the implementation of reasoning mechanisms. Even if several web ontologies, such as Open-IoT, Saref, IoT-ontology, etc., are devoted to that purpose, they were challenging to implement and instantiate in different contextual situations. The objective of this work is twofold. First, we propose an ontology that ensures the description of the IoT system. In this ontology, we defined the four ontologies; person ontology, devices ontology, properties ontology, and functional ontology. Second, we come up with a conceptual adaptation that makes this ontology extendable core-domain. This approach has been evaluated through two case studies to depict the flexibility ensured by the proposed ontology for conceptual adaptation.
  Keywords: IoT; ontology design; adaptation; semantic; web service.
  
  
• Towards the generalisation of the generation of answerable questions from ontologies for education  
  by Toky Hajatiana Rabonary, Steve Wang, C. Maria Keet 
  Abstract: Generating questions automatically from ontologies, and marking thereof, may support teaching and learning activities and therewith alleviate a teachers workload. Numerous studies considered this for MCQs; however, learners also have to be confronted with, for instance, yes/no and short answer questions. We investigated ten types of educationally valuable questions. For each question type, we determined the axiom prerequisites to be able to generate and answer it and declared a set of template specifications as question sentence plans. Three algorithmic approaches were devised for generating the text from the ontology: semantics-based with 1) template variables using foundational ontology categories, or 2) using main classes from the domain ontology, and 3) generation mostly driven by NLP techniques. User evaluation demonstrated that option three far outperformed the ontology-based ones on syntactic and semantic correctness of the generated questions, and it generated 98.45% of the questions from all valid axiom prerequisites in our experiment.
  Keywords: ontology-based question generation; ontologies for education; natural language generation.
  
  
• 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.