ions of the world’s entities we come to experience and know. Facts, in this view are occurrences or states of affairs and may be a descriptive part of an explanation, but not the deep Why. Aristotle’s view, such as in Posterior Analytics provides a more familiar view of explanation as part of a logical, deductive, process using reason to reach conclusions. Aristotle proposed 4 types of causes (αι’τία) to explain things. These were from either the thing’s matter, form, end, or changeinitiator (efficient cause) (Falcon, 2006). Following Descartes, Leibniz and especially Newton, modern deterministic causality using natural mechanisms became central to causal explanations. To know what causes an event means to employ natural laws as the central means to understand and explain why it happened. As this makes clear, some notions of the nature of knowledge, namely, how we come to know something and the nature of reality, are parts of explanation. For example, John Stuart Mill provides a deductivist account of explanation as evidenced by these two quotes: “An individual fact is said to be explained, by pointing out its cause, that is by stating the law or laws of causation, of which its production is an instance,” and “a law or uniformity of nature is said to be explained, when another law or laws are pointed out, of which that law is but a case, and from which it could be deduced (Mill 1843).” While explainability has always be a concern of computer systems, the issue has became especially relevant with the success of artificial intelligence (AI) algorithms, such as deep neural networks, whose functioning is too opaque and complex to be understood easily even by those who developed them. This could limit general acceptance of and trust in these algorithms in spite of their advantages and wide range of applicability. Explainable AI (XAI) is an active research area whose goal is to provide AI systems with some degree of explainability. In “Explainable Artificial Intelligence: An Overview,” Sargur N. Srihari surveys the field of XAI. Explanations provided by XAI methods take a variety of forms, ranging from traditional feature-based explanations to “heat-map” visualizations, from illustrative examples to probabilistic modeling. Clearly, XAI is an exciting new area at the frontiers of AI. When computers were developed, one of the earliest questions was whether they might eventually be as intelligent as humans. The field of AI was created not only to investigate this question but also to actually develop systems that achieved it. A fundamental aspect of human intelligence is that we have “common sense,” and the study of this aspect of intelligence has been a part of AI from the beginning. AI has also always emphasized the benefits of providing explanations for system reasoning While commonsense knowledge (CSK) and its associated reasoning processes would seem to be useful for explainability, CSK research has, until recently, been more concerned with knowledge representation than with explainability. In “Commonsense and Explanation: Synergy and Challenges in the Era of Deep Learning Systems” by Gary Berg-Cross, the connections between CSK and explanations are discussed, including the challenges and opportunities. The goal is to achieve fluid explanations that are responsive to changing circumstances, based on commonsense knowledge about the world. The healthcare enterprise involves many different stakeholders – consumers, healthcare professionals and providers, researchers, and insurers. Sources of health related data are highly diverse and have many levels of granularity. As a result of the COVID-19 pandemic, healthcare issues that were previously only discussed by specialists are now part of the everyday discourse of the average individual. In “Applied Ontologies for Global Health Surveillance and Pandemic Intelligence,” Christopher J. O. Baker, Mohammad Sadnan Al Manir, Jon Hael Brenas, Kate Zinszer, and Arash Shaban-Nejad use Malaria surveillance as a use case to highlight the contribution of applied ontologies for enhancing enhanced interoperability, interpretability and explainability. These technologies are relevant for ongoing pandemic preparedness initiatives. Financial institutions are very complex entities that play many roles and have many kinds of stakeholders, ranging from customers, to regulators, to shareholders, and to the society as a whole. Given these many responsibilities, it is no surprise that financial institutions “have a lot of explaining to do,” as Michael Bennett so deftly begins his article “Financial Industry Explanation” where he presents some of the challenges of providing meaningful explanation in this domain. Explanations are a special case of the more general requirement of accountability which is becoming an issue for many other domains as well. The lessons learned by the financial industry explainability are likely to be valuable for other domains as well. Ontologies play a significant role in all of the many research projects referenced by papers in this special issue. However, the ontologies for explainability in XAI, commonsense reasoning, health surveillance, and finance do not seem to have much in common with one another. The final paper, “Decision Rationales as Models for Explanations” by Kenneth Baclawski, attempts to weave the various strands of ontologies for explainability together in a single reference ontology by focusing on the observation that the purpose of most of the systems is to make decisions, and that it is the decisions that need to be explained. Processes today, whether they are based on software or human activities or a combination of them, or whether they use legacy systems or newly developed systems seldom include explainability. In nearly all cases, explanations are neither recorded nor can be easily generated. Unfortunately, explainability cannot simply be added as another module. Rather it should drive every process from the earliest stages of planning, analysis and design. Explainability requirements must be empirically discovered during these stages (Clancey 2019). Unfortunately, currently there is little sensitivity to the need for explainability and little experience with addressing it. It is hoped that this special issue will assist stakeholders to develop their systems so that they provide meaningful explanations.

In this paper, we look at a number of new technologies that are making their way through the well-known ‘hype cycle’, along with a cold hard look at the hype cycle itself. The items explored include blockchain and artificial intelligence, with a detailed look at various manifestations of these. We learn to think of each new thing not in terms of the claims or even the vocabulary of its proponents, but through the lens of something called ‘ontology’. We explain what ontology is in the simplest sense of the word, this being a formal representation of real things. We consider how to use this as a governance framework with the organisation, including its application to business strategy. In a curious twist, we find that there is also a technology related to ontology that is making its own way up the hype curve, with promising applications for finding out things that are hidden in data. Of course, this is also called ‘ontology’. Finally, we pull this together to set out some points of departure for readers to think about how to manage meaning across the organisation and how to thereby understand and manage these and other new technologies as they come along.

With the increasing amount of software devoted to industrial automation and process control, it is becoming more important than ever for systems to be able to explain their behavior. In some domains, such as financial services, explainability is mandated by law. In spite of this, explanation today is largely handled in an unsystematic manner, if it is handled at all. The decisions of modern artificially intelligent systems, such as those built on deep neural networks, are especially difficult to explain. The goal of the recent Ontology Summit 2019 was concerned with the role of ontologies for explaining the functioning of a system. More specifically, the Ontology Summit focused on critical explanation gaps and the role of ontologies for dealing with these gaps. The sessions examined current technologies and real needs driven by risks and requirements to meet legal or other standards. The sessions covered explainable artificial intelligence, commonsense reasoning and knowledge, the role of narrative, and explanations in the fields of finance and medicine. The goal of this Communiqué is to foster research and development of approaches to explanations and to drive towards explanation support which can be incorporated into both knowledge engineering processes and ontology design best practices.

In this paper we report on the current stage of our design of methodologies and techniques to use conceptual ontologies to extract operational ontologies. The work begins from the paper “Using Mathematical Model Theory to Align Conceptual and Operational Ontologies in FIBO” and reports on two major extensions on that work. The first is the addition of a context framework to implement the satisfiability and interpretation requirements of our earlier model. We briefly discuss our current thinking on this context framework. We then work through an example using some of the contexts from the framework to show how this works in the case of an exchange commitment in the air travel industry. This example illustrates the unfolding of commitment at the level of the conceptual ontology into the right commitment and the obligation commitment at the level of the operational ontology. We then reach some conclusions which will direct us to future work.

This paper addresses the relationship between a conceptual ontology and an operational ontology. To date there has been little agreement about what the distinction between these and little consideration about how a conceptual ontology can be operationalized into an operational ontology and vice versa. Where it arises, the discussion is often about whether a particular ontology is a conceptual ontology or an operational ontology. While that discussion is interesting in itself, it masks a deeper discussion. That discussion occurs when a conceptual ontology is created which is intended to be operationalized in a variety of settings. In this paper, we consider the situation of the Financial Industry Business Ontology (FIBO) as promulgated by the Enterprise Data Management Council (EDMC). FIBO’s intended use is as a conceptual model which would be operationalized in a variety of projects by various players in the financial industry. Those operationalizations lead to structures which are represented in some form of first order logic, such as OWL. We use model theory to formalize the relationships between the symbols of the operationalization and the interpretation of those symbols in the conceptual ontology.

There are many connections among artificial intelligence, learning, reasoning and ontologies. The Ontology Summit 2017 explored, identified and articulated the relationships among these areas. As part of the general advocacy of the Ontolog Forum to bring ontology science and engineering into the mainstream, we endeavored to abstract a conversational toolkit from the Ontology Summit sessions that may facilitate discussion and knowledge sharing amongst stakeholders concerned with the topic. Our findings are supported with examples from the various domains of interest. The results were captured in the form of this Communiqué, with expanded supporting material provided on the web.

Software is critical to the majority of functionality in avionics and aerospace systems. The amount of safety-related software in avionics is growing rapidly (doubling in size around every four years), and the costs of software programmes in industry are increasingly unaffordable – safety-related code can cost upwards of USD $150 per line. At the same time, demands from avionics customers for increased scope and new functionality is increasing, and quality is non-negotiable: it is fixed by standards and safety requirements. The SECT-AIR project is addressing these cost and demand issues by focusing on automation in software engineering, with particular emphasis on model-based development. In this paper we provide an overview of the motivation behind the project, which started in 2016, and some of the key tasks it will carry out to help improve productivity, increase customer scope and maintain quality.

It is generally agreed that the interpretation of information, in any form, is context-dependent. The goal of the recent Ontology Summit 2018 was to explore the various relationships spanning ontology and context. This article is the end product of the summit and associated symposium. It describes motivation for creating explicit, formal context specifications, and discusses approaches for finding, understanding and formalizing context. We present this work and associated materials with the goal to foster the research and development of approaches to contexts and to drive towards context-aware solutions which can be incorporated into both knowledge engineering processes and ontology design best practices.

Since the beginnings of the Semantic Web, ontologies have played key roles in the design and deployment of new semantic technologies. Yet over the years, the level of collaboration between the Semantic Web and Applied Ontology communities has been much less than expected. Within Big Data applications, ontologies appear to have had little impact. These communities, along with the Linked Data community, all share the need for a common semantic understanding and a formal representation of the domains being studied, but they have taken very different approaches to deal with the challenges of large scale applications and linking of vast heterogeneous data. Because of this situation, the Ontology Summit 2014 focused on building bridges between these four communities. It was felt that identifying and overcoming ontology engineering bottlenecks is critical for all of these communities. This special issue is an effort to continue the process that began in 2014. The papers in this issue are concerned with the various aspects of the barriers identified at the Ontology Summit, and propose approaches for addressing them.

Ontologies and related reasoning systems are key to the facilitation of semantic integration and interoperability. But several key questions are involved. How do we define the tools, methodologies and frameworks which support this interoperability? What is required to achieve optimal performance ac ross applications and domains? How do we frame the conversation when discussing the role of ontologies in support of interoperability with various stakeholders? Ontology Summit 2016 explored these questions. This document is a summary of the Ontology Summit, where we present an overview of semantic interoperability challenges, provide examples from several domains, discuss design approaches, strategies and next steps for the field moving forward.

This paper describes a hackathon event that took place as part of the Ontology Summit in 2014. The purpose of this hackathon was to develop an integrated ontology for a potential travel risk application, and was intended as an exercise in ontology integration and re-use. The hackathon took place over a 48 hour period via remote teleconferencing. Several existing ontologies and ontology design patterns were integrated into one common framework, while a new ontology was developed from available data and integrated with these. The notion of ` event' as it relates to risk presented some interesting ontological issues that are described here. This paper describes the work that took place, gives a brief overview of the ontology content, and concludes with lessons learned on the opportunities and challenges in re-use of ontologies from different sources, including the implications of different ontological commitments. The final work product was substantial enough to form the basis of a possible travel risk application.

This paper considers how to frame different kinds of ontology, in the broader concept of the different dimensions of models in general. A key motivating factor has been to cut across conversations about what may be called an "ontology", by setting out the features and uses of different kinds of artifacts that are characterized as ontologies. As such this work aims to integrate various well-established perspectives on ontology development and usage, in such a way that practitioners in industry can work from this directly. This is further extended to consider the relationship between the different kinds of ontology and data. The framework described here is intended as the precursor to a more extensive methodology and is based on considerations of semiotics and ontological commitment. The goal is not to define new artifacts or perspectives, but to organize them and create a guideline for ontology development and re-use.

In response to the global financial crisis, the Basel Committee for Banking Supervision (BCBS) has published a set of principles for risk data aggregation reporting, known as BCBS239. This defines requirements for systemically important financial institutions to demonstrate mature governance of data. At the core of these is the need for common language. The optimum way to meet this common language requirement is something called an ‘ontology’, ie a business resource that uses formal logic to define concepts. Developing a business ontology requires techniques not typically found in information technology. This paper shows how a business ontology can be used for agile and timely risk data aggregation reporting. A freely available standard called the Financial Industry Business Ontology (FIBO) lays much of the groundwork for creating this kind of resource within a financial institution.

Mark Underwood a, Michael Gruninger b, Leo Obrst c,∗, Ken Baclawski d, Mike Bennett e, Gary Berg-Cross f, Torsten Hahmann g and Ram Sriram h a Krypton Brothers, Port Washington, NY, USA b University of Toronto, Toronto, Canada c The MITRE Corporation, McLean, VA, USA d Northeastern University, Boston, MA, USA e Hypercube Ltd, London, UK f Knowledge Strategies, Washington, DC, USA g University of Maine, Orono, ME, USA h National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA

Leo Obrst a,∗, Michael Gruninger b, Ken Baclawski c, Mike Bennett d, Dan Brickley e, Gary Berg-Cross f, Pascal Hitzler g, Krzysztof Janowicz h, Christine Kapp i, Oliver Kutz j, Christoph Lange k, Anatoly Levenchuk l, Francesca Quattri m, Alan Rector n, Todd Schneider o, Simon Spero p, Anne Thessen q, Marcela Vegetti r, Amanda Vizedom s, Andrea Westerinen t, Matthew West u and Peter Yim v a The MITRE Corporation, McLean, VA, USA b University of Toronto, Toronto, Canada c Northeastern University, Boston, MA, USA d Hypercube Ltd., London, UK e Google, London, UK f Knowledge Strategies, Washington, DC, USA g Wright State University, Dayton, OH, USA h University of California, Santa Barbara, Santa Barbara, CA, USA i JustIntegration, Inc., Kissimmee, FL, USA j Otto von Guericke University Magdeburg, Magdeburg, Germany k University of Bonn, Bonn, Germany; Fraunhofer IAIS, Sankt Augustin, Germany l TechInvestLab.ru, Moscow, Russia m The Hong Kong Polytechnic University, Hong Kong n University of Manchester, Manchester, UK o PDS, Inc., Arvada, CO, USA p University of North Carolina, Chapel Hill, NC, USA q Arizona State University, Phoenix, AZ, USA r INGAR (CONICET/UTN), Santa Fe, Argentina s Criticollab, LLC, Durham, NC, USA t Nine Points Solutions, LLC, Potomac, MD, USA u Information Junction, Fareham, UK v CIM Engineering, Inc., San Mateo, CA, USA

The goal of the Ontology Summit 2013 was to create guidance for ontology developers and users on how to evaluate ontologies. Over a period of four months a variety of approaches were discussed by participants, who represented a broad spectrum of ontology, software, and system developers and users. We explored how established best practices in systems engineering and in software engineering can be utilized in ontology development.

ProblemCurrently, there is no agreed on methodology for development of ontologies, and there is no consensus on how ontologies should be evaluated.Consequently, evaluation techniques and tools are not widely utilized in the development of ontologies.This can lead to ontologies of poor quality and is an obstacle to the successful deployment of ontologies as a technology. ApproachThe goal of the Ontology Summit 2013 was to create guidance for ontology developers and users on how to evaluate ontologies.Over a period of four months a variety of approaches were discussed by participants, who represented a broad spectrum of ontology, software, and system developers and users.We explored how established best practices in systems engineering and in software engineering can be utilized in ontology development. ResultsThis document focuses on the evaluation of five aspects of the quality of ontologies: intelligibility, fidelity, craftsmanship, fitness, and deployability.A model for the ontology life cycle is presented, and

This article describes the Financial Industry Business Ontology (FIBO) as a set of formal models that define unambiguous shared meaning for financial industry concepts. An account is given of the history and development of the FIBO series of standards and the theoretical underpinnings of these as a business or ‘conceptual’ model. Some initial proof of concept work is described, demonstrating how in addition to the use of FIBO as a conceptual model, it is possible to derive semantic technology-based applications that may be used to carry out novel types of processing on data. The development roadmap of the FIBO series of standards within the Object Management Group is also described, so that readers can have an idea of what to expect from FIBO and when.