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Ontologies define the main concepts and relations of a domain and can play the role of common language between domain experts, software developers and computer systems, allowing for easier and more comprehensive data management. Ontologies can provide a structure and context for data, enabling better analysis and decision‐making. Ontologies can be leveraged for improving various Machine Learning‐based tasks (they can be used for improving the accuracy and consistency of training data, and we can combine ML‐based predictions with ontology‐based reasoning). Ontologies are key components for achieving semantic data integration. In the context of this deliverable, we have surveyed 40 ontologies and 7 other knowledge organization systems related to food safety and we have categorized them according to a set of appropriate criteria. Subsequently we analysed the 18 case studies, that could involve ontologies, and for each one we have described the possible use of ontologies and what would be the benefit. Finally the identified case studies have been evaluated with respect to a set of criteria regarding benefits, cost and maturity. This publication is linked to the following EFSA Supporting Publications articles: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2024.EN-9118/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2024.EN-9119/full This publication is linked to the following EFSA Journal article: http://onlinelibrary.wiley.com/doi/10.2903/j.efsa.2024.e221201/full [ABSTRACT FROM AUTHOR]

Archival research is a complicated task that involves several diverse activities for the extraction of evidence and knowledge from a set of archival documents. The involved activities are usually unconnected, in terms of data connection and flow, making difficult their recursive revision and execution, as well as the inspection of provenance information at data element level. This article proposes a workflow model for holistic data management in archival research: from transcribing and documenting a set of archival documents, to curating the transcribed data, integrating it to a rich semantic network (knowledge graph), and then exploring the integrated data quantitatively. The workflow is provenance-aware, highly recursive and focuses on semantic interoperability, aiming at the production of sustainable data of high value and long-term validity. We provide implementation details for each step of the workflow and present its application in maritime history research. We also discuss relevant quality aspects and lessons learned from its application in a real context. [ABSTRACT FROM AUTHOR]

The integration of the classical Web (of documents) with the emerging Web of Data is a challenging vision. In this paper we focus on an integration approach during searching which aims at enriching the responses of non-semantic search systems with semantic information, i.e. Linked Open Data (LOD), and exploiting the outcome for offering advanced exploratory search services which provide an overview of the search space and allow the users to explore the related LOD. We use named entities identified in the search results for automatically connecting search hits with LOD and we consider a scenario where this entity-based integration is performed at query time with no human effort and no a-priori indexing which is beneficial in terms of configurability and freshness. However, the number of identified entities can be high and the same is true for the semantic information about these entities that can be fetched from the available LOD. To this end, in this paper we propose a Link Analysis-based method which is used for ranking (and thus selecting to show) the more important semantic information related to the search results. We report the results of a survey regarding the marine domain with promising results, and comparative results that illustrate the effectiveness of the proposed (PageRank-based) ranking scheme. Finally, we report experimental results regarding efficiency showing that the proposed functionality can be offered even at query time. [ABSTRACT FROM AUTHOR]