John Beverley

Research in STEM education is rapidly growing. Many terms in use have no shared meanings, making knowledge dissemination and accumulation difficult, if not impossible. With new terms constantly added to the literature, and often inconsistently used, there is a need for a common framework of STEM education terms. There is also a need to “take stock” of what has been accomplished in the field. To that end, we propose the adoption of standards and techniques from the field of ontology engineering to construct a shared vocabulary system that defines a universe of terms and formal relations among them, to address this challenge. We created a reference ontology, STEM Identity Ontology (STEMIO), and a domain ontology, Science Teacher Identity Ontology (STIO), to demonstrate how ontologies can be leveraged to improve existing STEM identity data and future research in the area. Ontology engineering has been successfully deployed across research in medicine, engineering, social science, and education; it has potential to facilitate research in STEM education.

We outline initial work aligning the Basic Formal Ontology (BFO) and the Information Exchange Standard (IES), illustrating how key design patterns and content structures can be connected across the two frameworks. Our analysis highlights points of ontological convergence and divergence, guided by scenarios and supported by formal modeling. The study contributes to methodologies for mapping between upper-level ontologies and standards-based models while providing insights for communities aiming to improve semantic interoperability.

We introduce a framework for representing information about entities that do not exist or may never exist, such as those involving fictional entities, blueprints, simulations, and future scenarios. Traditional approaches that introduce “dummy instances” or rely on modal logic are criticized, and a proposal is defended in which the cases in question are modeled using the intersections of actual types rather than specific non-existent tokens. The paper positions itself within the Basic Formal Ontology paradigm and its realist commitments, emphasizing the importance of practical, implementable solutions over purely metaphysical or philosophical proposals. We argue that existing approaches to non-existent entities either overcommit to metaphysical assumptions or introduce computational inefficiencies that hinder applications. By developing a structured ontology-driven approach to unreal patterns, the paper aims to provide a useful and computationally viable means of handling references to hypothetical or non-existent entities.

This paper critically examines the relationship between Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO), offering a comprehensive discussion of their theoretical foundations, design patterns, and implementation practices. The paper highlights BFO’s commitment to realism, perspectivalism, fallibilism, and adequatism, and illustrates how these principles guide the representation of domain-specific entities within CCO. The modular structure of CCO is analyzed, emphasizing its eleven component ontologies and the hub-and-spoke strategy that promotes semantic integration across diverse domains. The paper also engages with practical challenges in distinguishing between TLOs, MLOs, and domain ontologies, proposing heuristic and formal criteria for delineating their scope. Using a dataset from the US Federal Aviation Administration, the paper demonstrates CCO’s modeling capabilities, particularly in integrating design specifications and real-world data. The study concludes by underscoring the importance of aligning data quality and semantic interoperability in ontology engineering, and it calls for sustained methodological rigor and collaboration to advance the BFO-CCO ecosystem.

We propose a definition of capability as a class intermediate between function and disposition as the latter are defined in Basic Formal Ontology (BFO). A disposition inheres in a material entity and is realized in a certain kind of process. An example is the disposition of a glass to break when struck, which is realized when it shatters. A function is a disposition which is (simply put) the rationale for the existence of its bearer. To say for example that a water pump has the function to pump water is to say that the pump exists because something was needed that would pump water. Capabilities are a special sort of disposition in that that, like functions, they can be evaluated on the basis of how well they are realized. They differ from functions in that their realizations are not the rationale – not the primary reason – for the existence of their bearers. Thus, a water pump may have many capabilities, including to be weatherproof, to run without lubricant, and so forth, but only one function. All functions are capabilities on the view we defend, but not all capabilities are functions. We develop a series of axioms to distinguish capabilities formally from both dispositions and functions and provide examples of the use of ‘capability’ in a variety of domains.

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.

The term “credential” encompasses educational certificates, degrees, certifications, and government-issued licenses. An occupational credential is a verification of an individual’s qualification or competence issued by a third party with relevant authority. Job seekers often leverage such credentials as evidence that desired qualifications are satisfied by their holders. Many U.S. education and workforce development organizations have recognized the importance of credentials for employment and the challenges of understanding the value of credentials. In this study, we identified and ontologically defined credential and credential-related terms at the textual and semantic levels based on the Occupation Ontology (OccO), a BFO-based ontology. Different credential types and their authorization logic are modeled. We additionally defined a high-level hierarchy of credential related terms and relations among many terms, which were initiated in concert with the Alabama Talent Triad (ATT) program, which aims to connect learners, earners, employers and education/training providers through credentials and skills. To our knowledge, our research provides for the first time systematic ontological modeling of the important domain of credentials and related contents, supporting enhanced credential data and knowledge integration in the future.

This paper introduces a set of terms that are intended to act as an interface between cyber ontologies (like a file system ontology or a data fusion ontology) and top- and mid-level ontologies, specifically Basic Formal Ontology and the Common Core Ontologies. These terms center on what makes cyberinformation management unique: numerous acts of copying items of information, the aggregates of copies that result from those acts, and the faithful members of those aggregates that represent all other members.

Background Within the Open Biological and Biomedical Ontology (OBO) Foundry, many ontologies represent the execution of a plan specification as a process in which a realizable entity that concretizes the plan specification, a “realizable concretization” (RC), is realized. This representation, which we call the “RC-account”, provides a straightforward way to relate a plan specification to the entity that bears the realizable concretization and the process that realizes the realizable concretization. However, the adequacy of the RC-account has not been evaluated in the scientific literature. In this manuscript, we provide this evaluation and, thereby, give ontology developers sound reasons to use or not use the RC-account pattern. Results Analysis of the RC-account reveals that it is not adequate for representing failed plans. If the realizable concretization is flawed in some way, it is unclear what (if any) relation holds between the realizable entity and the plan specification. If the execution (i.e., realization) of the realizable concretization fails to carry out the actions given in the plan specification, it is unclear under the RC-account how to directly relate the failed execution to the entity carrying out the instructions given in the plan specification. These issues are exacerbated in the presence of changing plans. Conclusions We propose two solutions for representing failed plans. The first uses the Common Core Ontologies ‘prescribed by’ relation to connect a plan specification to the entity or process that utilizes the plan specification as a guide. The second, more complex, solution incorporates the process of creating a plan (in the sense of an intention to execute a plan specification) into the representation of executing plan specifications. We hypothesize that the first solution (i.e., use of ‘prescribed by’) is adequate for most situations. However, more research is needed to test this hypothesis as well as explore the other solutions presented in this manuscript.

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, a mapping methodology and set of alignments are presented according to specific criteria which prioritize semantic and logical principles. The ontology alignments are evaluated by checking their logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the alignment criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles.

Mid-level ontologies are used to integrate data across disparate domains using vocabularies more specific than top-level ontologies and more general than domain-level ontologies. There are no clear, defensible criteria for determining whether a given ontology should count as mid-level, because we lack a rigorous characterization of what the middle level of generality is supposed to contain. Attempts to provide such a characterization have failed, we believe, because they have focused on the goal of specifying what is characteristic of those single ontologies that have been advanced as mid-level ontologies. Unfortunately, single ontologies of this sort are generally a mixture of top- and mid-level, and sometimes even of domainlevel terms. To gain clarity, we aim to specify conditions for membership in what we call the middle architecture, which consists solely of mid-level ontologies.

The explosion of interest in large language models (LLMs) has been accompanied by concerns over the extent to which generated outputs can be trusted, owing to the prevalence of bias, hallucinations, and so forth. Accordingly, there is a growing interest in the use of ontologies and knowledge graphs to make LLMs more trustworthy. This rests on the long history of ontologies and knowledge graphs in constructing human-comprehensible justification for model outputs as well as traceability concerning the impact of evidence on other evidence. Understanding the nature of arguments and argumentation is critical to each, especially when LLM output conflicts with what is expected by users. The central contribution of this article is to extend the Arguments Ontology (ARGO) - an ontology specific to the domain of argumentation and evidence broadly construed - into the space of LLM fact-checking in the interest of promoting justification and traceability research through the use of ARGO-based ‘blueprints’.

In our daily lives, as in science and in all other domains, we encounter huge numbers of dispositions (tendencies, potentials, powers) which are realized in processes such as sneezing, sweating, shedding, melting. Among this plethora of what we can think of as ‘mere dispositions’ is a subset of dispositions in whose realizations we have an interest – a car responding well when driven on ice, a rabbit’s lungs responding well when it is chased by a wolf, and so on. We call the latter ‘capabilities’ and we attempt to provide an ontological account of what capabilities are that is of sufficient generality to serve a variety of purposes, for example by providing a useful extension to ontology-based research in areas where capabilities data are being collected in siloed fashion.

This APA Blog Substack Newsletter extends a discussion with Barry Smith, who is a University at Buffalo (UB) professor of philosophy. Today Smith and his UB colleague and Co-Director of the National Center for Ontological Research John Beverley lead what one might call the academic arm of the Department of Defense and Intelligence Community Ontology Working Group (DIOWG). They outline here how ontology is serving as a career path for logicians and others interested in real-world applications of philosophy.

Moeser suggested participants default to linear ordering elements but they can be primed to impose either linear or partial ordering. This study seems problematic insofar as ‘greater than’ might be understood to incline participants to favor linear orderings. Recent follow-up studies strongly suggest participants do not default to linear ordering. It seems plausible, moreover, that the observed priming effect is far more pervasive than Moeser countenanced. The present work explores the extent to which priming for linear or partial orders conflicts with the construction of linear or partial ordering lists. Our two experiments suggest friction between priming and task, and also show once the priming tasks are cleared of potentially confounding semantic content, participants perform equally with respect to constructing linear or partial orders under similar priming conditions.

Although the last decade has seen a proliferation of ontological approaches to arguments, many of them employ ad hoc solutions to representing arguments, lack interoperability with other ontologies, or cover arguments only as part of a broader approach to evidence. To provide a better ontological representation of arguments, we present the Arguments Ontology (ArgO), a small ontology for arguments that is designed to be imported and easily extended by researchers who work in different upper-level ontology frameworks, different logics, and different approaches to argument evaluation. Unlike most ontological approaches to arguments, ArgO utilizes Basic Formal Ontology (BFO) as an upper-level ontology, and may be used alongside other commonly used ontologies in the BFO framework, including boththe Information Artifact Ontology (IAO), and the Information Entity Ontology (INFO). Critically, our proposal is principled, based on rigorous definitions and formal axioms out of which characterizations of arguments naturally fall. It is our hope that ArgO may assist researchers in many projects, including: integrating heterogeneous sources of evidence, structuring the content of semantic wikis, and enhancing semantic reasoning.

The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies––structured, controlled, vocabularies––are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.

The field of AI has undergone a series of transformations, each marking a new phase of development. The initial phase emphasized curation of symbolic models which excelled in capturing reasoning but were fragile and not scalable. The next phase was characterized by machine learning models—most recently large language models (LLMs)—which were more robust and easier to scale but struggled with reasoning. Now, we are witnessing a return to symbolic models as complementing machine learning. Successes of LLMs contrast with their inscrutability, inaccuracy, and hallucinations, which underwrite concerns over the reliability and trustworthiness of these systems, motivating investigations into commonsense reasoning, AI explainability, and formal verification techniques. Moreover, proper assessments of hybrid machine learning/symbolic systems require novel strategies to facilitate comparisons of performance and guide future AI progress. The EDGeS AAAI 2023 Spring symposium brought together researchers focusing on novel assessments and benchmarks for evaluating hybrid and artificial general intelligence (AGI) systems. This symposium revealed what was already suspected: research concerning evaluation of machine learning/symbolic hybrids and AGI is, unfortunately, lacking. Even so, the discussion was fruitful.

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.

Imagine my surprise at discovering - tucked inside the cover of a first edition Alice in Wonderland – an unknown dialogue written by Lewis Carroll himself! It was scribbled on the back of a napkin, punctuated by Carroll’s tell-tale signature, and seems to have been written hastily. Carroll is known among laypersons as an absurdist, but he’s esteemed among formal thinkers as impressively logical. You can probably then imagine my further surprise at discovering various fallacies and confusions in the dialogue! I counted equivocations over use/mention, identity/predication, measurements, and lexical/material parthood, but I’m sure there are more hiding. If you share my interest in untangling these errors, I’ve copied Carroll’s dialogue below. I’ve found the task best pursued with help, whether students, interns, sisters, tarot readers, etc. As you’ll see, I’ve divided the dialogue into five sections, of varying difficulties. I recommend dividing helpers accordingly, assigning one section per group, then directing each to identify both good and bad reasoning in the section. After some time, groups should pass their discoveries to other groups for scrutiny. For example, if group 1 examined section 1, they’d pass their discoveries to group 2 who then checks group 1’s work against section 1. Proceed until each group has examined each section and – hopefully – you’ll find all the errors. Happy hunting!

Daniel R. DeNicola’s Moral Philosophy: A Contemporary Introduction begins (pg. ix) with an anonymous reviewer’s quote, paraphrased here: ‘The author of an introductory ethics textbook has an impossible task, creating a work both accessible to undergraduates and rigorous enough for philosophers.’ DeNicola can of course be forgiven for not achieving this impossible task. Still, some attempts are better than others. After surveying the text content, I explain why I discourage instructors from using this textbook in introductory ethics courses.

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology in early 2020. As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

There is more responsibility on heaven and earth than dreamt of in most philosophy. This dissertation explores three debates in three sub-fields of philosophy, highlighting in each responsibilities agents find themselves with whether they like it or not. In the chapter "Trust Logic, Not Tortoises", I propose an answer to Wright’s Justification Question – to what extent are we justified in our knowledge of logic? – arguing early knowledge of logic is a species of know-how underwritten by dispositions to infer that are themselves often refined into more sophisticated dispositions to infer as we develop. Given how foundational such know-how is to our understanding of the world, we find ourselves responsible for cultivating more sophisticated reasoning, whatever its provenance. In the chapter Ties that Undermine, I argue attributions to an agent of moral responsibility are largely influenced by the perceived rareness of that agent's ability to provide aid or prevent harm, degree of aid provided or harm prevented, and cost to that agent in providing aid or preventing harm. Voluntary adoption of such moral responsibility is not necessary for an agent to be morally responsible. I generalize this result to the epistemic domain in the chapter Speak No Evil: Understanding Hermeneutical (In)justice, arguing attributions to an agent of epistemic responsibility are also influenced by the perceived rareness of that agent's ability to generate true beliefs in themselves and others, extent of benefit provided and harm prevented by having such true beliefs, and cost to that agent in generating such true beliefs. Here, we see voluntary adoption of such epistemic responsibility is not necessary for an agent to be epistemically responsible. By exploring the relevance of such responsibility to three distinct debates in three distinct sub-fields of philosophy, this dissertation highlights how pervasive responsibility is to the lives of agents, and the work of philosophers.

Basic Formal Ontology (BFO) is a top-level ontology consisting of thirty-six classes, designed to support information integration, retrieval, and analysis across all domains of scientific investigation, presently employed in over 350 ontology projects around the world. BFO is a genuine top-level ontology, containing no terms particular to material domains, such as physics, medicine, or psychology. In this paper, we demonstrate how a series of cases illustrating common types of change may be represented by universals, defined classes, and relations employing the BFO framework. We provide discussion of these cases to provide a template for other ontologists using BFO, as well as to facilitate comparison with the strategies proposed by ontologists using different top-level ontologies.

Miranda Fricker's original presentation of Hermeneutical Injustice left open theoretical choice points leading to criticisms and subsequent clarifications with the resulting dialectic appearing largely verbal. The absence of perspicuous exposition of hallmarks of Hermeneutical Injustice might suggest scenarios exhibiting some – but not all – such hallmarks are within its purview when they are not. The lack of clear hallmarks of Hermeneutical Injustice, moreover, obscures both the extent to which Fricker's proposed remedy Hermeneutical Justice – roughly, virtuous communicative practices – adequately addresses the injustice, and the accuracy of criticisms suggesting that Hermeneutical Justice is insufficient to the task. In what follows, after briefly defending necessary and sufficient conditions for what I take to be the best candidate interpretation of Hermeneutical Injustice, I build on recent work on moral responsibility to construct and defend a rigorous explication of Hermeneutical Justice.