In this article, we advocate the use of “epistemic” ontologies, i.e., systems of categories representing our knowledge of the world, rather than the world directly. We first expose a metaphysical framework based on a dual mental and physical realism, which underpins the development of these epistemic ontologies. To this end, we refer to the theories of intentionality and representation established within the school of Franz Brentano at the turn of the 20th century and choose to rehabilitate the notion of a (...) ‘representation object’, as theorized by Kasimir Twardowski. We therefore propose that the categories of epistemic ontologies correspond to ‘general representation objects’. Secondly, we apply these proposals to the treatment of technical artefacts, material qualities of objects and events (notably as a continuation of our previous work on events). This leads us to sketch out a foundational epistemic ontology. (shrink)

In this paper, we challenge the vision of creating a giant knowledge graph of reusable research data from multiple projects and platforms, based on Semantic Web technologies and a shared conceptualisation in the form of a commonly used ontology ecosystem, promoted by the Semantic Data for Humanities and Social Sciences project (SDHSS). Three main issues are raised: the difficulty of proposing a cross-disciplinary ontology for humanities and social sciences given the different research agendas and constructivist approaches; the difficulty of interlinking (...) the objects present in the different information systems, due to different notions of identity; the challenge of dealing with uncertainty, inconsistencies and contradictions in information sources, when the aim is to produce ‘factual’ information. First, I propose an epistemological analysis of information, as distinct from data and knowledge, then I develop a conceptualisation of information in humanities and social sciences, based on the OntoClean methodology, and the foundational ontologies DOLCE and Descriptions and Situations, and show how it can ground a cross-disciplinary ontology ecosystem based on the CIDOC CRM and its SDHSS integration. Finally, I apply these principles to a detailed analysis of the information production process and show how the proposed conceptualisation provides possible solutions to the three challenges raised. (shrink)

Biases in cognition are ubiquitous. Social psychologists suggested biases and stereotypes serve a multifarious set of cognitive goals, while at the same time stressing their potential harmfulness. Recently, biases and stereotypes became the purview of heated debates in the machine learning community too. Researchers and developers are becoming increasingly aware of the fact that some biases, like gender and race biases, are entrenched in the algorithms some AI applications rely upon. Here, taking into account several existing approaches that address the (...) problem of implicit biases and stereotypes, we propose that a strategy to cope with this phenomenon is to unmask those found in AI systems by understanding their cognitive dimension, rather than simply trying to correct algorithms. To this extent, we present a discussion bridging together findings from cognitive science and insights from machine learning that can be integrated in a state-of-the-art semantic network. Remarkably, this resource can be of assistance to scholars (e.g., cognitive and computer scientists) while at the same time contributing to refine AI regulations affecting social life. We show how only through a thorough understanding of the cognitive processes leading to biases, and through an interdisciplinary effort, we can make the best of AI technology. (shrink)

The notion of affordance remains elusive, notwithstanding its importance for the representation of agency, cognition, and behaviors. This paper lays down a foundation for an ontology of affordances by elaborating the idea of “core affordance” which would serve as a common ground for explaining existing diverse conceptions of affordances and their interrelationships. For this purpose, it analyzes M. T. Turvey’s dispositional theory of affordances in light of a formal ontology of dispositions. Consequently, two kinds of so-called “core affordances” are proposed: (...) specific and general ones. Inspired directly by Turvey’s original account, a specific core affordance is intimately connected to a specific agent, as it is reciprocal with a counterpart effectivity (which is a disposition) of this agent within the agent-environment system. On the opposite, a general core affordance does not depend on individual agents; rather, its realization involves an action by an instance of a determinate class of agents. The utility of such core affordances is illustrated by examining how they can be leveraged to formalize other major accounts of affordances. Additionally, it is briefly outlined how core affordances can be employed to analyze three notions that are closely allied with affordances: the environment, image schemas, and intentions. (shrink)

Classifying domain entities into their respective top-level ontology concepts is a complex problem that typically demands manual analysis and deep expertise in the domain of interest and ontology engineering. Using an efficient approach to classify domain entities enhances data integration, interoperability, and the semantic clarity of ontologies, which are crucial for structured knowledge representation and modeling. Based on this, our main motivation is to help an ontology engineer with an automated approach to classify domain entities into top-level ontology concepts using (...) informal definitions of these domain entities during the ontology development process. In this context, we hypothesize that the informal definitions encapsulate semantic information crucial for associating domain entities with specific top-level ontology concepts. Our approach leverages state-of-the-art language models to explore our hypothesis across multiple languages and informal definitions from different knowledge resources. In order to evaluate our proposal, we extracted multi-label datasets from the alignment of the OntoWordNet ontology and the BabelNet semantic network, covering the entire structure of the Dolce-Lite-Plus top-level ontology from most generic to most specific concepts. These datasets contain several different textual representation approaches of domain entities, including terms, example sentences, and informal definitions. Our experiments conducted 3 study cases, investigating the effectiveness of our proposal across different textual representation approaches, languages, and knowledge resources. We demonstrate that the best results are achieved using a classification pipeline with a K-Nearest Neighbor (KNN) method to classify the embedding representation of informal definitions from the Mistral large language model. The findings underscore the potential of informal definitions in reflecting top-level ontology concepts and point towards developing automated tools that could significantly aid ontology engineers during the ontology development process. (shrink)

yamato sharply distinguishes itself from other existing upper ontologies in the following respects. (1) Most importantly, yamato is designed with both engineering and philosophical minds. (2) yamato is based on a sophisticated theory of roles, given that the world is full of roles. (3) yamato has a tenable theory of functions which helps to deal with artifacts effectively. (4) Information is a ‘content-bearing’ entity and it differs significantly from the entities that philosophers have traditionally discussed. Taking into account the modern (...) society in which a flood of information occurs, yamato has a sophisticated theory of informational objects (representations). (5) Quality and quantity are carefully organized for the sake of greater interoperability of real-world data. (6) The philosophical contribution of yamato includes a theory of objects, processes, and events. Those features are illustrated with several case studies. These features lead to the intensive application of yamato in some domains such as biomedicine and learning engineering. (shrink)

Sustainability in systems refers to applying sustainable principles and practices to create more resilient, efficient, and equitable systems that promote the well-being of people and the planet. Sustainability is an essential topic in contemporary software engineering, and its relationship with the characteristics and properties of a system or product called quality attributes is still an open question since each researcher has established their definition of sustainability in software. This has created diverse terms and concepts for distinct application environments and scopes, (...) creating ambiguity and misconceptions. This work defines a domain ontology of Sustainability in Software named SinSO to address these issues. SinSO was implemented in OWL, using competency-based questions to validate. The findings show that this proposal satisfies several quality and content requirements. Also, using Protégé and the Hermit reasoner, we verified that SinSO is consistent since the ontology statements are coherent and do not lead to conflicting or contradictory conclusions. In addition, competency questions allowed us to demonstrate that SinSO does fulfill its purpose. FOCA methodology allowed us to evaluate SinSO quality. Also, SinSO was used in two case studies, one about software for senior-citizen smart-home, and the other, a simulator to develop and test smart-city applications, achieving positive outcomes. To verify its accuracy, completeness, and maintainability, further evaluations of SinSO are needed in real case studies. We conclude that SinSO can significantly contribute to reducing ambiguity and enhancing comprehension in this area. Furthermore, SinSO can be an effective tool for engineers to recognize the concepts and relationships in the sustainable domain to consider in the systems development life cycle to build sustainable systems. (shrink)