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Extending Thompson's discussion of the extended self, I contrast his view if the I Self (in William James' terms) with my focus on James' Me Self. I go on to discuss the Aggregate Self, the Digital Self, and various concepts of plural selves and cultural differences. Thompson's introduction of distributed self fits nicely here and further expands the discussion of self. I introduce the concept of the Extended Object and briefly examine thanobots and AI and the way they may affect our concepts of self in the future. [ABSTRACT FROM AUTHOR]

Maintenance records of complex industrial equipment contain a large amount of unstructured data (e.g. technician notes) pertaining to repair actions and associated equipment sub-components, degradation conditions, failure mechanisms, etc. These unstructured data can yield valuable insights to improve the equipment design and maintenance plans, resulting in higher productivity and lower operating costs. Since manual review of information is time-consuming, companies make limited use of the maintenance records. To address this opportunity, we propose a taxonomy-guided method for automatically analysing the unstructured data and inferring critical information, specifically the hierarchy of the equipment's sub-assemblies and constituent parts that malfunctioned or failed during a breakdown event. Our method leverages syntactic (related to word frequency) as well as semantic (related to word co-occurrence and their meaning) knowledge. A novel contribution of our work is that we provide a confidence score for the information inferred by our method. Only the maintenance records which receive a low confidence score will require manual review to confirm the automated method's results, thus ensuring minimal use of human resources. We demonstrate the performance of our method using a real-world data set from equipment used in oil rigs. [ABSTRACT FROM AUTHOR]

Introduction: Modern forestry increasingly relies on the management of large datasets, such as forest inventories and land cover maps. Governments are typically in charge of publishing these datasets, but they typically employ disparate data formats (sometimes proprietary ones) and published datasets are commonly disconnected fromother sources, including previous versions of such datasets. As a result, the usage of forestry data is very challenging, especially if we need to combine multiple datasets. Methods and results: Semantic Web technologies, standardized by the World Wide Web Consortium (W3C), have emerged in the last decades as a solution to publish heterogeneous data in an interoperable way. They enable the publication of self-describing data that can easily interlink with other sources. The concepts and relationships between them are described using ontologies, and the data can be published as Linked Data on the Web, which can be downloaded or queried online. National and international agencies promote the publication of governmental data as Linked Open Data, and research fields such as biosciences or cultural heritage make an extensive use of SemanticWeb technologies. In this study, we present the result of the European Cross-Forest project, addressing the integration and publication of national forest inventories and land cover maps from Spain and Portugal using Semantic Web technologies. We used a bottom-up methodology to design the ontologies, with the goal of being generalizable to other countries and forestry datasets. First, we created an ontology for each dataset to describe the concepts (plots, trees, positions, measures, and so on) and relationships between the data in detail. We converted the source data into Linked Open Data by using the ontology to annotate the data such as species taxonomies. As a result, all the datasets are integrated into one place this is the Cross-Forest dataset and are available for querying and analysis through a SPARQL endpoint. These data have been used in real-world use cases such as (1) providing a graphical representation of all the data, (2) combining it with spatial planning data to reveal the forestry resources under themanagement of Spanish municipalities, and (3) facilitating data selection and ingestion to predict the evolution of forest inventories and simulate how different actions and conditions impact this evolution. Discussion: The work started in the Cross-Forest project continues in current lines of research, including the addition of the temporal dimension to the data, aligning the ontologies and data with additional well-known vocabularies and datasets, and incorporating additional forestry resources. [ABSTRACT FROM AUTHOR]

Interventions addressing more than one health behaviour at a time could be an efficient way of intervening to manage chronic conditions. Within a systematic review of multiple health behaviour change (MBHC) interventions, we identified key components of interventions in patients with chronic conditions, assessed how they are linked to theory, behaviour change techniques implemented, and evaluated their impact on intervention effectiveness. Studies were identified by systematically searching five electronic databases. Subgroup analyses and meta-regressions were conducted to analyse the association between intervention components and behavioural changes. In total, 61 studies were included spanning different chronic conditions (e.g., cardiovascular conditions, type 2 diabetes). Most interventions sought to change behaviours simultaneously (72%), often targeting the ‘physical activity, diet and smoking’ cluster of behaviours (33%), and were not theory informed (55%). A total of 36 behaviour change techniques were identified, most commonly goal setting behaviour and self-monitoring of behaviour. Subgroup analyses indicated that MHBC interventions delivered entirely face-to-face might not be as effective for physical activity outcomes, and not using goal setting (behaviour) might be more effective for smoking cessation outcomes. Meta-regressions indicated that a longer intervention duration may work best to achieve better physical activity outcomes. This review provides a comprehensive understanding of interventions and contributes to the field of MHBC by facilitating data-driven insights for future optimisation and dissemination. [ABSTRACT FROM AUTHOR]

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms. [ABSTRACT FROM AUTHOR]

Background: Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources. Results: We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues. Conclusions: These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process. [ABSTRACT FROM AUTHOR]

Environmental relationships were critical to most early colonial encounters, especially for those involving permanent settlements. The ability to successfully establish a colony required developing relationships with plants, animals, and the land because they were central to providing for the colonists' basic subsistence needs. The ways European colonizers developed these new relationships rested on their moral ecologies, a mix of beliefs and practices which give a sense of right and wrong to actions that are informed by ontologies. In this paper we use the concept of moral ecologies to explore the relationships that two empires—the British in New England and the Spanish in New Mexico—created as they established colonies that also entangled Indigenous peoples. The differences in moral ecologies between these empires and the peoples they invaded help explain tensions in the past that remain powerful today. [ABSTRACT FROM AUTHOR]

To improve data usage in an interdisciplinary context, a clear understanding of the variables being measured is required for both humans and machines. In this paper, the I-ADOPT framework, which decomposes variable names into atomic elements, was tested within the context of continental surfaces and critical zone science, characterized by a large number and variety of observed environmental variables. We showed that the I-ADOPT framework can be used effectively to describe environmental variables with precision and that it was flexible enough to be used in the critical zone science context. Variable names can be documented in detail while allowing alignment with other ontologies or thesauri. We have identified difficulties in modeling complex variables, such as those monitoring fluxes between different environmental compartments and for variables monitoring ratios of physical quantities. We also showed that, for some variables, different decompositions were possible, which could make alignments with other ontologies and thesauri more difficult. The precision of variable names proved inadequate for data discovery services and a non-standard label (SimplifiedLabel) had to be defined for this purpose. In the context of open science and interdisciplinary research, the I-ADOPT framework has the potential to improve the interoperability of information systems and the use of data from various sources and disciplines. [ABSTRACT FROM AUTHOR]

Language technologies are gaining momentum as textual information saturates social networks and media outlets, compounded by the growing role of fake news and disinformation. In this context, approaches to represent and analyse public speeches, news releases, social media posts and other types of discourses are becoming crucial. Although there is a large body of literature on text-based machine learning, it tends to focus on lexical and syntactical issues rather than semantic or pragmatic. Being useful, these advances cannot tackle the nuanced and highly context-dependent problems of discourse evaluation that society demands. In this paper, we present IAT/ML, a metamodel and modelling approach to represent and analyse discourses. IAT/ML focuses on semantic and pragmatic issues, thus tackling a little researched area in language technologies. It does so by combining three different modelling approaches: ontological, which focuses on what the discourse is about; argumentation, which deals with how the text justifies what it says; and agency, which provides insights into the speakers' beliefs, desires and intentions. Together, these three modelling approaches make IAT/ML a comprehensive solution to represent and analyse complex discourses towards their understanding, evaluation and fact checking. [ABSTRACT FROM AUTHOR]

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data management. We detail OntoBuilder's architecture, core functionalities, and application scenarios, demonstrating its proficiency and adaptability in addressing complex ontological challenges. Our empirical assessment highlights OntoBuilder's strengths in enabling seamless visualization, automated ontology generation, and robust semantic integration, thereby significantly enhancing user workflows and data management capabilities. The performance of the linked data tools across multiple metrics further underscores the effectiveness of OntoBuilder. [ABSTRACT FROM AUTHOR]

Background: Since 2011, the Lesbian and Gay Liberation in Canada project has sought to make the history of the LGBTQ2+ movement in Canada more accessible to a wide range of users. Analysis: We review approaches we have taken to further our vision, from digitization to graph databases, ontology development, and public engagement through Wikipedia edit-a-thons. Conclusions and implications: Having created a robust interface for sharing LGBTQ2+ history, we are able to focus our attention on building up publicly available information on marginalized communities, while also strategizing for the long-term sustainability of our project. [ABSTRACT FROM AUTHOR]

Drug traceability is a critical process involving monitoring and validation of the origin, quality, and safety of pharmaceutical products throughout their supply chain to prevent the distribution of counterfeit, substandard, or expired drugs that could harm patients. Traditional centralized solutions for drug traceability, relying on intermediaries and central authorities, introduce risks of data manipulation, corruption, and single points of failure. This work presents the design and implementation of a novel solution for decentralized drug traceability based on blockchain technology and on a reputation mechanism that operates on top of a trustworthy decentralized knowledge base, thus integrating three core technologies: blockchain, semantic, and reputation methods. Blockchain technologies ensure transparent and secure supply chain processes while providing a trustworthy estimation of the reputation of supply chain participants. Semantic technologies address drug data heterogeneity by ensuring interoperability and creating mappings between various data sources, including verifying the identities of the various users. Additionally, the reputation mechanism promotes transparency and accountability, as stakeholders contribute feedback on drug quality, authenticity, and reliability. This fosters a culture of trust and reliability, offering the drug supply chain an effective tool for continuous improvement and informed decision-making based on aggregated feedback, ultimately enhancing overall quality and safety throughout the distribution network. The design and implementation of the system, along with several evaluations, show the feasibility of the new semantic blockchain system in real-world scenarios and the improvement of the entities with a high reputation score. Our solution is more trustworthy, discouraging fraudulent activities as security is based on the various properties included in the semantic model. [ABSTRACT FROM AUTHOR]

To implement Open Governance a crucial element is the efficient use of the big amounts of open data produced in the public domain. Public administration is a rich source of data and potentially new knowledge. It is a data intensive sector producing vast amounts of information encoded in government decisions and acts, published nowadays on the World Wide Web. The knowledge shared on the Web is mostly made available via semi-structured documents written in natural language. To exploit this knowledge, technologies such as Natural Language Processing, Information Extraction, Data mining and the Semantic Web could be used, embedding into documents explicit semantics based on formal knowledge representations such as ontologies. Knowledge representation can be made possible by the deployment of Knowledge Graphs, collections of interlinked representations of entities, events or concepts, based on underlying ontologies. This can assist data analysts to achieve a higher level of situational awareness, facilitating automated reasoning towards different objectives, such as for knowledge management, data maintenance, transparency and cybersecurity. This paper presents a new ontology d2kg [d(iavgeia) 2(to) k(nowledge) g(raph)] integrating in a unique way standard EU ontologies, core and controlled vocabularies to enable exploitation of publicly available data from government decisions and acts published on the Greek platform Diavgeia with the aim to facilitate data sharing, re-usability and interoperability. It demonstrates a characteristic example of a Knowledge Graph based representation of government decisions and acts, highlighting its added value to respond to real practical use cases for the promotion of transparency, accountability and public awareness. The developed d2kg ontology in owl is accessible at: http://w3id.org/d2kg , as well as documented at: http://w3id.org/d2kg/documentation. [ABSTRACT FROM AUTHOR]

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This article describes the application of ontological and cognitive modeling in the research of "elastic resilience" of energy and ecological systems. It outlines the processes of ontological and cognitive engineering, provides examples of ontologies and cognitive maps, and suggests a method for integrating ontological, cognitive, and mathematical modeling of the fuel and energy complex (FEC) in the resilience studies of energy systems (ES). It also discusses the results of computational experiments to assess the resilience of energy and ecological systems, including the use of machine learning methods. [ABSTRACT FROM AUTHOR]

The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions. [ABSTRACT FROM AUTHOR]

Abstract Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea . It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Abstract Background Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources. Results We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues. Conclusions These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions.

Over the last thirty years, In the field of digital philology, several generic IT tools have been developed to produce digital scholarly editions (DSE). However, the majority of DSEs continue to be produced using custom-developed tools. This trend is unsustainable, but it is because generic tools are often less usable, difficult to customize, and unsuitable for the scientific objectives of scholars. The scientific community has been exploring how to create flexible and effective tools for digital philology. Two main responses have emerged in the literature: the need for shared editorial models that represent philologists’ desiderata, and the adoption of software engineering practices to produce more robust, durable, and easily maintainable tools. This article presents a modeling strategy that considers both perspectives and is based on the creation of a network of editorial models, each one theoretically framed within the philological approach in which it has emerged. The editorial models, formalized as ontologies, can be applied as conceptual models to determine the logical organization and functioning of a tool for digital philology, thanks to the principles of domain-driven design.

Project-based organizations (PBOs) rely on project management as their core business process. These organizations are characterized by their disaggregated structure and diverse authority distribution, leading to complications in synchronizing projects, governance, and functional divisions. In this regard, there is a need of a tool to manage this complexity. This paper introduces a novel approach – the IModel- for modeling, designing and analyzing PBOs, following the Design Science Research Method, that seeks to is creating an artifact that solves a real-world problem in a specific context. This artifact is developed through a conceptual integration between project management (PM) and enterprise architecture (EA) sources based on ontologies. Ontologies are powerful, machine-readable graph-structured models which enables sophisticated models’ reasoning and querying. IModel fuses the organizational domain from the ArchiMate EA language with PM insights from PMBOK 7th edition. The resulting IModel has been submitted to an expert’s judgement to assess its intended purposes as well, its potential to be applied in the real-world to support the PBO.

ABSTRACT: PROBLEMATIC ONTOLOGIES: WHICH TYPES OF CULTIVATED MEAT WOULD BE MORALLY WRONG TO PRODUCE AND EAT? The technology of cultivating cells from a donor animal biopsy makes it possible to consume meat without any creature having been killed in the process. Assuming that there are no valid moral arguments against this type of food practice, in this paper I ask whether specific types of cultivated meat production and consumption may legitimately raise moral concerns of a particular nature. Three cases are focused on: 1) human cultivated meat; 2) cultivated meat of domestic animals and 3) cultivated meat of extinct or endangered animals. The result of the investigation is that, contrary to appearances, none of these three cases is capable of raising moral problems that are peculiar and additional to those that might possibly concern cultivated meat in general.

Quantification of states, corporations, nature or self has become pervasive in the past 40 years. The water world’s struggles are rife with, and shaped by, numbers, indicators, metrics and models. This review explores how the production, promotion and use of 'water numbers' conceals deeply political processes, hypotheses, worldviews, intents, old habits and new fashions. Whether embodied in scientific or expert practices, or in indicators, thresholds, water accounts or cost–benefit analyses, water numbers promote specific values and interests; they also obfuscate complexity, heterogeneities and uncertainties, they manufacture legitimacy and authority, and they act as control devices to shape behaviour We offer a more detailed analysis of water indicators that describe water scarcity, ecological status, progress towards SDG 6, and embody New Public Management principles. We end with a call for critical water studies to more forcefully engage with these debates, in line with the centrality of quantification in water management and policy.

The advent of information technology re-innovates all sectors of bio-sciences. Researchers use Semantic Web to improve web searching, mining and integration, which alleviates the time-consuming task of finding relevant and high-quality content. Semantics is improved through ontology engineering in any domain. Amended and developed ontologies will be uploaded to existing standardised and approved biomedical repositories. The establishment of a World Wide Web Consortium (W3C) approved and standardised ontology repository is the most ambitious goal. This work will solely focus on some selected agri-ontologies. The main objective is to promote outcome-based research and transformation styles of relevant expertise sharing. The intended goal is to win project funding to train and equip students with relevant skills and expertise. Need-based and market-oriented training and professional grooming are a tangible asset for students. The majority of traditional Web development freelancers are unaware of ontology or semantic web market demand. Freelancing is another option for expert Ontology developers. However, agriculture students are used to all the research vocabulary and terminologies in their area, but they do not know how to contribute their expertise to improve the efficiency of the Semantic Web in their domain. If the improvement in relevant ontology becomes a part of the Semantic Web, then it is termed 'Real-time Web semantics enhancement'. In other words, the target ontology becomes a part of the future Web of meaning. [ABSTRACT FROM AUTHOR]

The ethics of artificial intelligence (AI) is a research topic with both theoretical and practical significance. However, the ethical and moral aspects of conceptual, ontological, semantic, and knowledge modeling, more specifically, and which are sometimes found in AI applications, is not being given sufficient attention. I argue that it should. Whether considering using or developing these meaning-focused models, there are ethical aspects. This paper offers a preliminary outline about this potentially new research field, discussing: some questions and areas of concern, their significance; scenarios of ethical import, and initial ethical guidelines. A focus area, potential case study, and lens through which to see the ethical significance at hand, is ontologies, with emphasis on the most abstract ontologies (so-called generic, foundational, top-level or upper ontologies). This would aim to show the ethical import of highly abstract (often philosophical) ontological choices, assumptions, and their effects on data, developers, users, and society. In particular, I stress the need for critical examination of such models, their content, their intended meanings, assumptions, ideologies, as well as the development process. Both the ethics of, and ethics in, these activities are, therefore, of interest. Topics in the ethics of AI that are relevant include: transparency, bias, data-sharing, privacy, control over data, etc. This paper thereby contributes to the ethics of AI, as well as the ethics of information, of knowledge managment and relevant topics by developing and discussing the ethics of ontology (and similar systems). Future development will refine and add to the ideas expressed in this paper. [ABSTRACT FROM AUTHOR]

Many industries and academia have devoted a lot of effort and money to creating and/or using good extract-transform-load (ETL) software suitable for their data analysis purposes since it is considered a key to their success. As a result, we find the valuable interventions of research efforts based on ETL software are divided according to well-known approaches such as Business Intelligence, Big Data, and/or Semantic. As a result, problems arise in keeping up with changes and handling the significant diversity in features across these approaches. Which results in disorientation in the finding, evaluation, and choice of an ETL for industries and academia facing their approaches needs. These problems inspire us to provide a contribution that uses the systematic-literature-review (SLR) method to collect 207 papers from three databases, namely, ScienceDirect, Springer, and IEEE, dated from 2010 to 2022, grouped based on both ETL approaches and their commonly used criteria, afterwards using an existing method that automatically identifies the adequate multicriteria method for this study, which gives us the analytical-hierarchy-process method to provide the best research paper according to the requirements of scientific literature. The result implies the great significance of this study in multiple ways, providing a global idea of research papers about ETL approaches, allowing customers to eliminate uncertainty from selecting an ETL according to their specific approach needs, preferences, and interests, and also enabling future researchers and developers of ETL to decide when to focus and how to make innovative contributions to fill gaps in the literature. [ABSTRACT FROM AUTHOR]

The evolution of digital media has increased the number of crimes committed using digital equipment. This has led to the evolution of the computer forensics area to digital forensics (DF). Such an area aims to analyze information through its main phases of identification, collection, organization, and presentation (reporting). As this area has evolved, many techniques have been developed, mainly focusing on the formalization of terminologies and concepts for providing a common vocabulary comprehension. This has demanded efforts on several initiatives, such as the definition of ontologies, which are a means to identify the main concepts of a given area. Hence, the existing literature provides several ontologies developed for supporting the DF area. Therefore, to identify and analyze the existing ontologies for DF, this paper presents a systematic literature review (SLR) in which primary studies in the literature are studied. This SLR resulted in the identification of ontology building methodologies, ontology types, feasibility points, evaluation/assessment methods, and DF phases and subareas ontologies have supported. These results were based on the analysis of 29 ontologies that aided in answering six research questions. Another contribution of this paper is a set of recommendations on further ontology-based support of DF investigation, which can guide researchers and practitioners in covering existing research gaps. [ABSTRACT FROM AUTHOR]

Project-based organizations (PBOs) rely on project management as their core business process. These organizations are characterized by their disaggregated structure and diverse authority distribution, leading to complications in synchronizing projects, governance, and functional divisions. In this regard, there is a need of a tool to manage this complexity. This paper introduces a novel approach – the IModel - for modeling, designing and analyzing PBOs, following the Design Science Research Method, that seeks to is creating an artifact that solves a real-world problem in a specific context. This artifact is developed through a conceptual integration between project management (PM) and enterprise architecture (EA) sources based on ontologies. Ontologies are powerful, machine-readable graph-structured models which enables sophisticated models' reasoning and querying. IModel fuses the organizational domain from the ArchiMate EA language with PM insights from PMBOK 7th edition. The resulting IModel has been submitted to an expert's judgement to assess its intended purposes as well, its potential to be applied in the real-world to support the PBO. [ABSTRACT FROM AUTHOR]

This paper presents a model and lessons learned for creating a cross-domain national ontology and Linked (Open) Data (LOD) infrastructure. The idea is to extend the global, domain agnostic "layer cake model" underlying the Semantic Web with domain specific and local features needed in applications. To test and demonstrate the infrastructure, a series of LOD services and portals in use have been created in 2002–2023 that cover a wide range of application domains. They have attracted millions of users in total suggesting feasibility of the proposed model. This line of research and development is unique due to its systematic national level nature and long time span of over twenty years. [ABSTRACT FROM AUTHOR]

Research has become increasingly reliant on extensive data. The integration, sharing and reuse of research data poses a significant challenge, particularly in the context of interdisciplinary collaborative projects. An essential objective for a research infrastructure dedicated to data management is to facilitate efficient data discovery and integration of diverse data sources. This pressing need for FAIR data requires, besides persistent identifiers and data citation rules, common standards and shared vocabularies, thesauri and ontologies. These knowledge artifacts, referred to as terminologies, often exist in disconnected and distributed forms. The work presented in this paper describes our terminology repository and service, enabling a unified access, development, and maintenance of terminologies within biodiversity and environmental sciences. We characterize use cases requirements for semantically enhanced components and applications and show where the adoption of the OntoPortal technology enabled us to cover those requirements in the context of our research infrastructure. [ABSTRACT FROM AUTHOR]

This paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous research to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all of the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarising the large production of scientific contributions on the topic. Then, a novel ontology named the Reactive Digital Twin Ontology is described, in which sensors, activators, and the decision processes are also semantically described, turning the previous synchronic approach to cultural heritage documentation into a diachronic one. Some case studies exemplify this theory. [ABSTRACT FROM AUTHOR]

This paper develops a critical perspective on the use of conversational agents (CAs) with children at home. Drawing on interviews with eleven parents of pre-school children living in Norway, we illustrate the ways in which parents resisted the values epitomised by CAs. We problematise CAs' attributes in light of parents' ontological perceptions of what it means to be human and outline how their attitudes correspond to Bourdieu's [1998a. Acts of Resistance. New York: New Press] concept of acts of resistance. For example, parents saw artificial conversation designed for profit as a potential threat to users' autonomy and the instant gratification of CAs as a threat to children's development. Parents' antecedent beliefs map onto the ontological tensions between human and non-human attributes and challenge the neoliberal discourse by demanding freedom and equality for users rather than productivity and economic gain. Parents' comments reflect the belief that artificial conversation with a machine inappropriately and ineffectively mimics a nuanced and intimate human-to-human experience in service of profit motives. [ABSTRACT FROM AUTHOR]

Competence-based approaches have received increased attention, as the demand for qualified people with the right combination of competences establishes itself as a major factor of organizational performance. This paper examines how competences can be incorporated into Enterprise Architecture modeling: (i) we identify a key set of competence-related concepts such as knowledge, skills, and attitudes, (ii) analyze and relate them using a reference ontology (grounded on the Unified Foundational Ontology), and (iii) propose a representation strategy for modeling competences and their constituent elements leveraging the ArchiMate language, discussing how the proposed models can fit in enterprise competence-based practices. Our approach is intended to cover two tasks relevant to the combined application of Enterprise Architecture and Competence Modeling: 'zooming in' on competences, revealing the relations between competences, knowledge, skills, attitudes and other personal characteristics that matter in organizational performance, and 'zooming out' of competences, placing them in the wider context of other personal competences and overall organizational capabilities. An assessment of the representation is offered in the form of an empirical survey. [ABSTRACT FROM AUTHOR]

Quantification of states, corporations, nature or self has become pervasive in the past 40 years. The water world’s struggles are rife with, and shaped by, numbers, indicators, metrics and models. This review explores how the production, promotion and use of 'water numbers' conceals deeply political processes, hypotheses, worldviews, intents, old habits and new fashions. Whether embodied in scientific or expert practices, or in indicators, thresholds, water accounts or cost–benefit analyses, water numbers promote specific values and interests; they also obfuscate complexity, heterogeneities and uncertainties, they manufacture legitimacy and authority, and they act as control devices to shape behaviour. We offer a more detailed analysis of water indicators that describe water scarcity, ecological status, progress towards SDG 6, and embody New Public Management principles. We end with a call for critical water studies to more forcefully engage with these debates, in line with the centrality of quantification in water management and policy. [ABSTRACT FROM AUTHOR]

Low-Code Development is a new software development paradigm which is typically used to generate web applications from high-level visual notations. These notations allow to express the User Interface and Application Logic (usersystem interactions) in a way which is understandable by the end-users. Using certain model-driven approaches, the low-code environments allow for generating the front-end layer and basic CRUD operations in the back-end. Yet still, non-standard domain logic (data processing) operations still necessitate the use of traditional programming. In this article we present a visual language, called RSL-DL that can be used to represent such non-standard domain logic in a visual form. It allows to capture domain knowledge with complex domain rules aligned with requirements models. The language synthesises and extends approaches found in knowledge representation (ontologies) and software modelling language engineering. The development environment of RSL-DL enables fully automatic generation of domain logic code by reasoning over and reusing domain knowledge. The environment includes a dedicated model editor and a transformation engine. The language's abstract syntax is defined using a meta-model expressed in MOF. Its semantics is expressed with several translational rules that map RSL-DL models onto typical programming language constructs. The article presents a list of these rules in an informal way, and then introduces their formalisation using a graphical transformation notation. The RSL-DL environment includes an inference engine that enables processing queries to domain models and selecting appropriate invocations to generated code. It was also initially validated through studies that involve understandability, operability and complexity assessment. Based on these results, we conclude that declarative knowledge representations can be successfully used to produce imperative backend code with non-trivial logic. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new framework, the Intensional–Ontological Model (IOM), for representing meaning, and especially for representing semantic change, in linguistic linked data resources. This framework, which makes use of previous work in the literature on lexical semantics and ontologies, is intended to help clarify what we mean when we model semantic change and to assist in elaborating different ontology patterns for doing so. In this work, we assume a simple architecture, one which is at the basis of the well-known OntoLex-Lemon vocabulary and which consists of one or more lexicons linked to an ontology. Our model, which is based on this architecture and informed by previous work on word senses and ontologies, is intended to provide a clear interpretation for the modelling of both onomasiological and semiasological changes, in both static and dynamic versions. This article describes how the IOM framework represents word meaning as the relationship between a word and an ontological concepts in the 'static' case, demonstrating that the IOM is compatible with OntoLex-Lemon (while at the same time providing a greater level of detail as to the meaning of the 'sense' and 'reference' relationships). It then goes on to detail how the IOM can help us understand how to model semantic shifts in linked data lexical resources with a focus on conceptual change and the addition of temporal information to semantic shift data. [ABSTRACT FROM AUTHOR]

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IntroductionModern forestry increasingly relies on the management of large datasets, such as forest inventories and land cover maps. Governments are typically in charge of publishing these datasets, but they typically employ disparate data formats (sometimes proprietary ones) and published datasets are commonly disconnected from other sources, including previous versions of such datasets. As a result, the usage of forestry data is very challenging, especially if we need to combine multiple datasets.Methods and resultsSemantic Web technologies, standardized by the World Wide Web Consortium (W3C), have emerged in the last decades as a solution to publish heterogeneous data in an interoperable way. They enable the publication of self-describing data that can easily interlink with other sources. The concepts and relationships between them are described using ontologies, and the data can be published as Linked Data on the Web, which can be downloaded or queried online. National and international agencies promote the publication of governmental data as Linked Open Data, and research fields such as biosciences or cultural heritage make an extensive use of Semantic Web technologies. In this study, we present the result of the European Cross-Forest project, addressing the integration and publication of national forest inventories and land cover maps from Spain and Portugal using Semantic Web technologies. We used a bottom-up methodology to design the ontologies, with the goal of being generalizable to other countries and forestry datasets. First, we created an ontology for each dataset to describe the concepts (plots, trees, positions, measures, and so on) and relationships between the data in detail. We converted the source data into Linked Open Data by using the ontology to annotate the data such as species taxonomies. As a result, all the datasets are integrated into one place this is the Cross-Forest dataset and are available for querying and analysis through a SPARQL endpoint. These data have been used in real-world use cases such as (1) providing a graphical representation of all the data, (2) combining it with spatial planning data to reveal the forestry resources under the management of Spanish municipalities, and (3) facilitating data selection and ingestion to predict the evolution of forest inventories and simulate how different actions and conditions impact this evolution.DiscussionThe work started in the Cross-Forest project continues in current lines of research, including the addition of the temporal dimension to the data, aligning the ontologies and data with additional well-known vocabularies and datasets, and incorporating additional forestry resources.