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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]

This report proposes a governance model for managing the activities involving ontologies at EFSA. It contains recommendations about (a) the scoping of the ontology development activities at EFSA (purpose and subject matter), (b) external and internal consistency and interoperability, (c) maintenance and evolution, (d) access policies, (e) IT considerations (aligned with Deliverable D4), (f) outsourcing strategy, (g) training needs related to ontologies. The proposed model is aligned with the existing governance activities of EFSA, such as Rebuild DF. The document is complemented with the adopted governance model of other ontologies. 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-9120/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]

This document describes the steps that required for creating and maintaining an ontology; for exploiting an ontology in order to describe data from one or more data sources; and for developing services and applications that provide unified access and insightful analysis on the data. The document provides the state of the art of the various tools and software components that exist and can be used for carrying out various tasks of the overall process. The components are grouped based on their intended use and for each one of them a concise description, as well as other useful details (i.e. if they are open‐source, their license, etc.), are provided. For each step or task, the involved software components are also described in a table presenting their details with respect to various dimensions (e.g. open‐source, license, available online, key advantages, etc.), in order to enable their quick comparison. Moreover, successful examples of semantic data integration are presented, i.e., for cases where a top‐level ontology is used for integrating data for the cultural and biodiversity domain, and services for large‐scale semantic data integration (including hundreds of datasets from several domains). Furthermore, it provides the high‐level architectural suggestions that can be used as a basis for the actual implementation of any ontology‐based system; the described processes and tools can be exploited for the development of the ontology‐based functionality. Finally, it offers the adaptations of the proposed architecture for the EFSA case studies, by providing details about their components and indicative pipelines and tools. This publication is linked to the following EFSA Supporting Publications articles: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2024.EN-9119/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2024.EN-9120/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]

Analyzing statements of facts and claims in online discourse is subject of a multitude of research areas. Methods from natural language processing and computational linguistics help investigate issues such as the spread of biased narratives and falsehoods on the Web. Related tasks include fact-checking, stance detection and argumentation mining. Knowledge-based approaches, in particular works in knowledge base construction and augmentation, are concerned with mining, verifying and representing factual knowledge. While all these fields are concerned with strongly related notions, such as claims, facts and evidence, terminology and conceptualisations used across and within communities vary heavily, making it hard to assess commonalities and relations of related works and how research in one field may contribute to address problems in another. We survey the state-of-the-art from a range of fields in this interdisciplinary area across a range of research tasks. We assess varying definitions and propose a conceptual model - Open Claims - for claims and related notions that takes into consideration their inherent complexity, distinguishing between their meaning, linguistic representation and context. We also introduce an implementation of this model by using established vocabularies and discuss applications across various tasks related to online discourse analysis. [ABSTRACT FROM AUTHOR]

The CIDOC Conceptual Reference Model (CIDOC-CRM) is an ISO Standard ontology for the cultural domain that is used for enabling semantic interoperability between museums, libraries, archives and other cultural institutions. For leveraging CIDOC-CRM, several processes and tasks have to be carried out. It is therefore important to investigate to what extent we can automate these processes in order to facilitate interoperability. For this reason, in this paper, we describe the related tasks, and we survey recent works that apply machine learning (ML) techniques for reducing the costs related to CIDOC-CRM-based compliance and interoperability. In particular, we (a) analyze the main processes and tasks, (b) identify tasks where the recent advances of ML (including Deep Learning) would be beneficial, (c) identify cases where ML has been applied (and the results are successful/promising) and (d) suggest tasks that can benefit from applying ML. Finally, since the approaches that leverage both CIDOC-CRM data and ML are few in number, (e) we introduce our vision for the given topic, and (f) we provide a list of open CIDOC-CRM datasets that can be potentially used for ML tasks. [ABSTRACT FROM AUTHOR]

Since the task of accessing RDF datasets through structured query languages like SPARQL is rather demanding for ordinary users, there are various approaches that attempt to exploit the simpler and widely used keyword-based search paradigm. However this task is challenging since there is no clear unit of retrieval and presentation, the user information needs are in most cases not clearly formulated, the underlying RDF datasets are in most cases incomplete, and there is not a single presentation method appropriate for all kinds of information needs. As a means to alleviate these problems, in this paper we investigate an interaction approach that offers multiple presentation methods of the search results (multiple-perspectives), allowing the user to easily switch between these perspectives and thus exploit the added value that each such perspective offers. We focus on a set of fundamental perspectives, we discuss the benefits from each one, we compare this approach with related existing systems and report the results of a task-based evaluation with users. The key finding of the task-based evaluation is that users not familiar with RDF (a) managed to complete the information-seeking tasks (with performance very close to that of the experienced users), and (b) they rated positively the approach. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame. In this survey, we focus on data‐driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under:Commercial, Legal, and Ethical Issues > Fairness in Data MiningCommercial, Legal, and Ethical Issues > Ethical ConsiderationsCommercial, Legal, and Ethical Issues > Legal Issues [ABSTRACT FROM AUTHOR]

Health-related information is nowadays accessible from many sources and is one of the most searched-for topics on the Internet. However, existing search systems often fail to provide users with a good list of medical search results, especially for classic (keyword-based) queries. In this article we elaborate on whether and how we can exploit biomedicine-related entities from the emerging Web of Data for improving (through reranking) the results returned by a search system. The aim is to promote relevant but low-ranked hits containing entities that are important to the current search context. We introduce an approach that is based on entity extraction applied on the retrieved documents, yielding a graph of documents along with entities, which in turn is analyzed probabilistically using a Random Walk-based method. The proposed approach is independent of the submitted query and the underlying retrieval models, and thus can be applied over any ranked list of medical search results. Evaluation results using the data set of TREC Clinical Decision Support track demonstrate that the proposed approach can significantly improve the results returned by classic and widely applicable retrieval models. The results also enabled us to identify cases where the proposed reranking method fails to improve the ranking. [ABSTRACT FROM AUTHOR]

Purpose – Marine species data are scattered across a series of heterogeneous repositories and information systems. There is no repository that can claim to have all marine species data. Moreover, information on marine species are made available through different formats and protocols. The purpose of this paper is to provide models and methods that allow integrating such information either for publishing it, browsing it or querying it. Aiming at providing a valid and reliable knowledge ground for enabling semantic interoperability of marine species data, in this paper the authors motivate a top level ontology, called MarineTLO and discuss its use for creating MarineTLO-based warehouses. Design/methodology/approach – In this paper the authors introduce a set of motivating scenarios that highlight the need of having a top level ontology. Afterwards the authors describe the main data sources (Fisheries Linked Open Data, ECOSCOPE, WoRMS, FishBase and DBpedia) that will be used as a basis for constructing the MarineTLO. Findings – The paper discusses about the exploitation of MarineTLO for the construction of a warehouse. Furthermore a series of uses of the MarineTLO-based warehouse is being reported. Originality/value – In this paper the authors described the design of a top level ontology for the marine domain able to satisfy the need for maintaining integrated sets of facts about marine species and thus assisting ongoing research on biodiversity. Apart from the ontology the authors also elaborated with the mappings that are required for building integrated warehouses. [ABSTRACT FROM AUTHOR]

Named Entity Extraction (NEE) is the process of identifying entities in texts and, very commonly, linking them to related (Web) resources. This task is useful in several applications, e.g. for question answering, annotating documents, post-processing of search results, etc. However, existing NEE tools lack an open or easy configuration although this is very important for building domain-specific applications. For example, supporting a new category of entities, or specifying how to link the detected entities with online resources, is either impossible or very laborious. In this paper, we show how we can exploit semantic information (Linked Data) at real-time for configuring (handily) a NEE system and we propose a generic model for configuring such services. To explicitly define the semantics of the proposed model, we introduce an RDF/S vocabulary, called 'Open NEE Configuration Model', which allows a NEE service to describe (and publish as Linked Data) its entity mining capabilities, but also to be dynamically configured. To allow relating the output of a NEE process with an applied configuration, we propose an extension of the Open Annotation Data Model which also enables an application to run advanced queries over the annotated data. As a proof of concept, we present X-Link, a fully-configurable NEE framework that realizes this approach. Contrary to the existing tools, X-Link allows the user to easily define the categories of entities that are interesting for the application at hand by exploiting one or more semantic Knowledge Bases. The user is also able to update a category and specify how to semantically link and enrich the identified entities. This enhanced configurability allows X-Link to be easily configured for different contexts for building domain-specific applications. To test the approach, we conducted a task-based evaluation with users that demonstrates its usability, and a case study that demonstrates its feasibility. [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]