Ebook: Formal Ontology in Information Systems

This volume contains all accepted papers presented at the 14th edition of the Formal Ontology in Information Systems conference (FOIS 2024). As with the previous edition, this fourteenth edition of the conference followed a sequentially hybrid approach, leveraging the best of an in-person conference with the additional opportunities offered by a virtual conference. FOIS 2024 is the first annual edition of the conference after several years of biennial events.

Building on the positive experience from the previous edition, FOIS 2024 solicited a diversity of papers in three broad categories: (1) foundational ontological issues, (2) methodological issues around the development, alignment, verification and use of ontologies; and (3) domain ontologies and ontology-based applications. In total, we received 56 full paper submissions of whic 19 were accepted after a thorough and deliberate review process (acceptance rate of 34%). Each paper was reviewed by at least three reviewers and the evaluations were then summarized by a meta-reviewer. Of the accepted papers, 16 were presented in-person in Enschede, The Netherlands, where the conference took place from 15 to 19 July. The remaining 3 papers were presented in the virtual portion of the conference, held from 8 to 9 July. The presented papers include 7 foundational papers (from 22 submissions), 4 applications and methods papers (from 15 submissions), and 8 domain ontology (from 19 papers).

The foundational papers addressed philosophical aspects of ontology design, such as exceptions, interpretations, holism and indeterminacy. The application category considered the application of ontology in specific fields, showing how ontology is used to solve problems or enhance understanding within various domains, such as climate science and ecosystems, manufacturing, medicine or physics. Some papers also consider domain ontologies in applied scenarios such as open science and social ontologies, or address hot topics in the state-of-the-art, such as ontologies and machine learning and large language models.

A number of additional papers which did not make it into these proceedings despite their high quality were recommended for presentation as part of the ontology showcase and demonstration track at the conference, or as part of the eight workshops co-located with the in-person conference in Enschede. These papers are published in a separate proceedings volume to appear in the CEUR proceedings series.

The conference had a rich and intensive program, with several parallel sessions on a range of topics (mixing papers from different tracks – main track, showcase and demonstrations): ontological analysis, meaning, interpretation and truth, domain and core ontologies, foundational ontologies, ontology applications, ontology engineering, theoretical issues and cognition, competence and intentionality. Five keynotes (one shared with ICBO), addressed different aspects of applied ontology and artificial intelligence: explainable machine learning, health AI, ontologies for machine learning, philosophical aspects of ontologies, and data sharing. Four panels: applied ontologies in the neuro-symbolic age (together with FOIS online), digital transformation with ontologies (industrial panel), and ontologies, creativity and AI completed the program.

The authors of the submitted papers come from 17 countries (in order of frequency, Germany, France, Brazil, Italy, USA, The Netherlands, Tunisia, Canada, India, South Africa, Austria, Czech Republic, England, Japan, Poland, Russia, Spain) and the program committee members from 18 countries, and together these came from all continents except Antarctica. Submissions to satellite events, such as the Early Career Symposium, the Ontology Showcase, and the fourteen collocated workshops attracted authors from several more countries.

We organized the papers in this volume by paper type, starting with papers focusing on foundational issues, followed by methodological papers and, finally, by domain ontologies and application papers.

Awards

Among all accepted papers, the PC chairs and a selection committee consisting of senior PC members chose two papers to be awarded prizes in recognition of their outstanding contribution and the exceptionally high quality of both the paper and presentation. The overall high quality of accepted papers made this selection quite difficult, but after thorough deliberation by the selection committee, the best paper award, which comes with a prize of 500 Euros, graciously sponsored by IOS Press, was awarded to the paper “Interpreting Texts and Their Characters” by Emilio M. Sanfilippo, Claudio Masolo, Emanuele Bottazzi and Roberta Ferrario. This paper introduces a novel approach to documenting interpretations of literary characters, grounded in empirical literary analysis, to closely reflect expert methodologies. By analyzing relationships between the names of fictional characters across various texts and authors, this approach effectively links ontological debates on identity with the interests of literary scholars. In addition, we awarded one distinguished paper award, which received a cash prize of 250 Euros sponsored by IAOA. The recipient was the paper “Meaning Holism and Indeterminacy of Reference in Ontologies” by Adrien Barton, Paul Fabry and Jean-François Ethier. This paper examines the extent of the severity of meaning holism for ontology engineering, based on several definitions of the meaning of a class term in an ontology, with regard to the classical analytic/synthetic distinction. Both prizewinning papers stand out for their remarkable cross-disciplinary contributions. They explore novel concepts in ontological and philosophical analysis, applying these ideas to advance ontology engineering. This underscores the dynamism of the Formal Ontology community, which continues to foster innovative research and interdisciplinary engagement.

Acknowledgements

The conference would not have been possible without all of the authors who submitted their work. We want to thank all authors, regardless of whether or not their paper was accepted, for their contribution to the building and sustaining of a community for applied ontology research. Equally important were the contributions of the program committee; over 21 senior PC and 55 PC members, who carefully reviewed and discussed all submissions.

As general chair, Giancarlo Guizzardi (University of Twente, The Netherlands) gave the main direction for this FOIS edition and played a major role in its coordination. The success of the conference also owes much to the local organizer Tiago Prince Sales (University of Twente, The Netherlands), and to all members of the amazing orange local team, and to the online chair, Sergio de Cesare (University of Westminster, UK) and all the other chairs who helped with publicity, workshops, the ontology showcase, and the early career symposium. The complete list of people who helped to organize FOIS 2024 is included after this preface.

We also warmly thank the invited speakers (Mieke Boon, Michel Dumontier, Frank Van Harmelen, Øystein Linnebo, and Barend Mons, Laura Daniele, Quirien Wijnands, Ivo Velitchkov, Wouter Franke), panelists (Pascal Hitzler, Pawel Garbacz, Fabian Neuhaus, Luciano Serafini, John Beverley, Maria Hedblom, Guendalina Righetti, Shenghui Wang, Greta Adamo) and moderators (Mehwish Alam, Mara Abel, Jan Voskuil, Renata Guizzardi) for making FOIS a very interactive conference.

We also take this opportunity to thank our partners and sponsors. First of all, we thank the International Association for Ontology and its Applications (IAOA) as the association that provides the funding and governing structure for the organization and guidance of the FOIS conference series and which sponsors the distinguished paper awards. We likewise thank IOS Press for their continued support in the publication of the FOIS proceedings and for their sponsorship of the best paper award. In particular, we thank the University of Twente for providing the infrastructure and for their support in organizing the event.

We are happy to end this preface with the announcement that, from now on, the FOIS conference will happen annually (not biennially, as previously) and that the next edition will be organized in Catania (Italy) in September 2025.

Cassia Trojahn

Daniele Porello

Situationally-aware artificial agents operating with competence in natural environments face several challenges: spatial awareness, object affordance detection, dynamic changes and unpredictability. A critical challenge is the agent’s ability to identify and monitor environmental elements pertinent to its objectives. Our research introduces a neurosymbolic modular architecture for reactive robotics. Our system combines a neural component performing object recognition over the environment and image processing techniques such as optical flow, with symbolic representation and reasoning. The reasoning system is grounded in the embodied cognition paradigm, via integrating image schematic knowledge in an ontological structure. The ontology is operatively used to create queries for the perception system, decide on actions, and infer entities’ capabilities derived from perceptual data. The combination of reasoning and image processing allows the agent to focus its perception for normal operation as well as discover new concepts for parts of objects involved in particular interactions. The discovered concepts allow the robot to autonomously acquire training data and adjust its subsymbolic perception to recognize the parts, as well as making planning for more complex tasks feasible by focusing search on those relevant object parts. We demonstrate our approach in a simulated world, in which an agent learns to recognize parts of objects involved in support relations. While the agent has no concept of handle initially, by observing examples of supported objects hanging from a hook it learns to recognize the parts involved in establishing support and becomes able to plan the establishment/destruction of the support relation. This underscores the agent’s capability to expand its knowledge through observation in a systematic way, and illustrates the potential of combining deep reasoning with reactive robotics in dynamic settings.

There are a number of standards aiming to facilitate the exchange of competence data in the educational and job market areas. Despite their relevance, we have observed that they could benefit from an in-depth analysis of the notion of competence, given its central role in the intended application areas. This includes addressing facets of competence not only when attributed to particular individuals, but also when required by occupations in general. A comprehensive account for competences should ideally account for competence-related elements such as knowledge, attitudes, skills. It should also countenance their performance—related to tasks, their context, and outcomes—as well as their evolution over time (in order to account for the notion of ‘proficiency’). While some of these aspects are addressed in the existing standards, they are addressed in a partial manner, and a comprehensive conceptualization that can serve as a reference for articulating the various perspectives is still lacking. This is the focus of Core-O as a well-founded competence reference ontology.

A significant portion of scientific knowledge resides within scholarly publications, both in print and digital formats. Recent advancements in natural language processing and information extraction techniques have enhanced the accessibility of this knowledge for further automated querying and processing. Structured and semantically-aware representations, such as ontologies, play a crucial role in simplifying and integrating access to this vast pool of knowledge. While several ontologies have been developed to capture the structure and discourse of scientific publications, there is a notable scarcity of ontologies for succinctly representing named entities that are present in scholarly documents.

This paper introduces the Ontology for Named Entity Representation (OnNER) to address this gap. OnNER is designed to represent named entities – the terms identified and labeled using named entity recognition (NER) methods – from scholarly publications. The ontology provides a structured semantic representation of the named entities, how they are labeled, and where they occur. We discuss the overall design of OnNER, its integration with other ontologies, and demonstrate how the ontology facilitates advanced querying of named entities’ presence and collocation within and across publications.

The Common Core Ontologies(CCO) are designed as a mid-level ontology suite that extends the Basic Formal Ontology. In 2017,CUBRC, Inc. made CCO openly available. CCO has since been increasingly adopted by a broad group of users and applications and is proposed as the first standard mid-level ontology. Despite these successes, documentation of the contents and design patterns of the CCO has been comparatively minimal. This paper is a step toward providing enhanced documentation for the mid-level ontology suite through a discussion of the contents of the eleven ontologies that collectively comprise the Common Core Ontology suite.

Energy is a fundamental phenomenon of physics, but energy also plays an important role in the representation of many domains, since many processes involve energy transformation or transfer. However, energy is represented very differently in existing ontologies. Even in ontologies that share BFO as top-level ontology, energy is sometimes treated as disposition, as quality, and as a material entity. As we discuss in the paper, there are reasons for each choice, which makes the ontological representation of energy a challenging subject.

In this paper we present an ontological analysis of energy in a BFO-based mid-level ontology (MENO), including the different kinds of energy, their relations to dispositions as well as their realisation in processes.

The distinction between abstract and concrete entities, especially its precise characterization, remains underexplored in foundational ontology research. This paper aims to constitute the initial steps towards a formal ontology of abstracta and concreta. We begin by presenting three existing criteria (epistemic, spatiotemporal and causal) for the abstract/concrete distinction. We illustrate them with some well-known upper ontologies. After examining the shared assumption by the three criteria that any entity is either abstract or concrete but not both, we develop an alternative and more general framework for formally representing abstract and concrete entities. In particular, we propose a relational account of them by introducing the relation of “concretization”. The pivotal idea is that being abstract (or concrete) amounts to being concretized by (or concretizing) some other entity. We also briefly discuss a concretization-based reinterpretation of the spatiotemporal criterion and universals.

Large language models are capable of translating natural language texts into context-free grammar languages. The paper presents an initial assessment of whether such models can be used to produce ontological theories that formalise natural language descriptions of certain situations. More specifically speaking, I will focus on translating a small set of natural language descriptions of some situations of ontological interests into a fixed formal ontological framework. The model I use will not be trained or fine-tuned for this purpose but prompted. In order to build the appropriate prompts I will take advantage of the formalisations from the 17th volume of the Applied Ontology journal, where six examples of such situations were formalised within the context of seven upper-level formal ontologies.

The paper presents a spatio-temporal ontology guided by a particular methodology, in which the semantics is constructed within a spatio-temporal interpretation structure that is built up in three stages. The first, stage stipulates a standard classical model of time and space. This structure forms the grounding for the interpretation. The next stage is the specification of domains of entities, which are either elements of the grounding structure (time points and regions) or constructions from these elements (mappings from time to space associated with individuals existing within the spatio-temporal structure). The final stage is the definition of conceptual vocabulary in terms of the grounding structure and the specified domains. This definitional stage can be further subdivided into three types of specification: direct grounding of primitives onto the underlying structure, indirect grounding by defining additional vocabulary in terms of grounded primitives, partial grounding by specifying semantics types and axioms to constrain the meaning of vocabulary that is not explicitly defined.

The main goal of the paper is to advocate a methodology rather than a specific ontology. We suggest that building up in this way, results in robust ontologies, whose assumptions can be clearly seen, since they are encapsulated within the grounding stage and domain specifications. Although the definitional stage may incorporate a diverse and expressive vocabulary, its terms are essentially just labels for properties and relations that were already implicit within the grounding structure.

Research in Digital Humanities calls for computational systems to document, compare, and analyze interpretations of cultural artifacts such as literary texts. These systems are intended to support scholars, critics, and students by facilitating access to existing analyses of texts, identifying similarities and divergences between interpretations, and more. We propose an approach for documenting interpretations of literary characters, grounded in the empirical practices of literary interpretation to align closely with experts’ methods. To achieve this, we remain neutral regarding the ontological status of characters, instead relying on formal approaches based on linguistics. We demonstrate how our approach can analyze relations between names of fictional characters across texts and authors, bridging discussions in analytic philosophy about identity with the interests of literary scholars.

Money is enigmatic. Despite extensive investigation to date, conflicting theories persist regarding its nature. Examples include views that “Money is a physical object,” “Money is an abstract concept,” and “Money is institutional status,” among others. This paper aims to unravel this mystery by engaging in an ontological discourse on the essence of money and constructing a three-layer model that comprises (i) the representation (legitimacy) layer, (ii) the role layer, and (iii) the property layer. In the representation layer, official state-issued objects such as banknotes are examined through the lens of the representation theory. In the role layer, these objects act as monetary role holders, referred to as monetary objects, by playing the monetary role which is inherently social in nature. The three fundamental properties/functions of money—serving as a unit of economic value, being exchangeable with commodities, and capable of being stored—are inherent in monetary role, and hence in monetary objects. However, these properties/functions remain latent until they are possessed by agents. The property layer elucidates that money is a contingent property of the owner of monetary objects, who can engage in economic activities by harnessing/actualizing these three properties/functions. In summary, our ontological theory of money posits Money as a property, Monetary objects holding the Monetary role, whose player is the Legitimate representing thing issued by the authority. For instance, a freshly minted 20 Euro banknote is a legitimate representing thing, transitioning into a monetary object upon holding the monetary role within the economic context. Its institutional/causal power becomes operative upon ownership by an agent. Our theory adopts a monistic perspective rather than a dualistic one, facilitated by the above nuanced distinctions made among entities pertaining to “money.” Discussion about how our theory works for resolving some of the current issues is presented together with a justification for the observation that money virtually has use value in addition to exchange value.

An engineering device or system is designed to retain its functioning conditions for the time it is needed. Unfortunately, failures happen and mark the state in which a device stops functioning to a satisfactory level and, thus, is considered malfunctioning. The study of malfunction and its prevention is a central topic in engineering and its analysis poses challenges relevant to applied ontology as well. In this work, we focus on the intersection between the conceptual and ontological understanding of causation and malfunction by studying existing works in these areas. Aiming to compare these approaches from a unified viewpoint, we introduce a causation-based analysis method to take on terminological and conceptual challenges. The goal is to develop a formal taxonomy of malfunctions which can also be used to compare the other approaches in the literature. Our work pays attention to real case scenarios paving the way to the ontological understanding and modeling of failures in practice. Furthermore, it helps exploiting the potentialities of failure analysis and, in particular, of root cause analysis.

The climate change assessment community relies on widely accepted definitions of risk and its components, e.g. hazard, exposure, and vulnerability, provided by the well-known international organisation Intergovernmental Panel on Climate Change (IPCC). Those definitions of risk have been changing through the years and are presented in a general and “common sense” form as they need to be understandable by the public society and accommodate notions of risk as embraced by different research streams. However, these definitions have proven ineffective in operational climate risk assessment procedures, which exposes the critical need for disambiguation. This paper addresses the lack of semantic clarity of risk and cognate concepts in the context of climate change assessment by unpacking the ontological commitments underlying the IPCC’s most recent definitions and glossary using the Common Ontology of Value and Risk (COVER) as a primary guideline. This study provides a more precise and refined ontological foundation of risk in climate change research that better aligns with the complexities of scenarios and assessments, and contributes to climate change research on mitigation and adaptation by supporting more effective communication and assessment of climate-related risks and humanity’s responses to them.

Semantic interoperability is a growing and challenging subject in the healthcare domain. It aims to ensure a coherent and unambiguous exchange, use, and reuse of health information among different systems and applications. In the context of the EUCAIM (Cancer Image Europe) project, semantic interoperability among various heterogeneous cancer image data models is required to support the communication, integration, and sharing of data in a standardized and structured way. For this purpose, hyper-ontology is developed as a common semantic meta-model that bridges the disparate imaging and clinical knowledge of the various repositories in EUCAIM and supports their integration. EUCAIM’s hyper-ontology is also an application-based ontology targeted for federated semantic querying and image annotation. To facilitate the hyper-ontology building process and ensure the extensibility of the ontology model, an iterative hybrid well-founded approach that divides the ontology structure into layers and modules is established.

Machine Learning (ML) models often operate as black-boxes, lacking transparency in their decision-making processes. Explainable Artificial Intelligence (XAI) aims to address the rationale behind these decisions, thereby enhancing the trustworthiness of ML models. In this paper, we propose an extension of the Explainable ML Workflows ontology, which was designed as a reference ontology with OntoUML, and implemented as an operational ontology with OWL. The Explainable ML workflows ontology reuses ML-Schema, which is a core ontology for representing ML algorithms. We have identified four main issues in the conceptualization of this ontology, namely the lack of feature categorization, the lack of data pre-processing methods, the shallow description of metadata related to training and testing, and the lack of detailed representation of XAI approaches and metrics. We addressed these four issues in the so-called Explainable ML Pipeline Ontology (XMLPO), which aims to provide a comprehensive description of the ML pipeline for XAI. XMLPO offers a deeper understanding of the entire ML pipeline, encompassing data input, pre-processing, model training and testing, and explanation processes. XMLPO was validated through a case study on the prediction of specific performance indicators in a manufacturing company, and the results of this validation showed that the ontology helps data scientists to better comprehend a ML pipeline and the features that influence the ML prediction model the most.

Diagrammatic and textual languages differ significantly with respect to the experience they offer to language users. While diagrammatic languages leverage visual variables to improve communication and problem solving, textual languages facilitate significantly a number of tasks including version control, model editing, model merging, parsing, etc. In this paper, we explore the design of a textual language for UFO-based ontologies, whose constructs mirror those of the OntoUML language. The language is supported by a rich VS Code-based editor, supporting (semantically-motivated) syntax verification, syntax highlight, autocomplete, and full integration into the OntoUML server ecosystem. A package manager is also offered to support ontology modularization and reuse, drawing inspiration from software package managers. Such functionality is currently not available to languages such as (Onto)UML and Semantic Web languages such as OWL.

Knowledge graphs (KGs) continue spreading into industrial use cases due to their advantages and superiority over classical data representations. A problem that has not yet adequately been solved for KGs is the traceability and provenance of changes, which can be required in an enterprise or by regulations. KGs typically contain the current snapshot of data valid at a certain moment in time only. Changes over time are usually not recorded and no change history exists. The lack of suitable and scalable traceability solutions hinders the wider application of KGs. This paper presents a traceability and provenance solution for KGs, which can track all changes of a KG on triple level. It comprises a provenance engine that intercepts SPARQL/Update queries; PROV-STAR, an RDF-star enabled light-weight extension of the Provenance Ontology (PROV-O) for representing changes and their provenance; and a SPARQL query transformation approach for tracking the changes on a separate provenance KG with SPARQL-star queries. The solution supports full traceability of all changes, on the lowest possible level of triples, with each change being comprehended with detailed provenance information. From the provenance KG a detailed change history can be retrieved, and any past version of the KG can be restored with a single query. The implementation and validation have shown that changes can be tracked at runtime during the normal operation of a KG. Furthermore, the solution is scalable to large KGs and frequent updates, as only the delta of each change is stored.