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Background The 10th and 9th revisions of the International Statistical Classification of Diseases and Related Health Problems (ICD10 and ICD9) have been adopted worldwide as a well-recognized norm to share codes for diseases, signs and symptoms, abnormal findings, etc. The international Consortium for Clinical Characterization of COVID-19 by EHR (4CE) website stores diagnosis COVID-19 disease data using ICD10 and ICD9 codes. However, the ICD systems are difficult to decode due to their many shortcomings, which can be addressed using ontology. Methods An ICD ontology (ICDO) was developed to logically and scientifically represent ICD terms and their relations among different ICD terms. ICDO is also aligned with the Basic Formal Ontology (BFO) and reuses terms from existing ontologies. As a use case, the ICD10 and ICD9 diagnosis data from the 4CE website were extracted, mapped to ICDO, and analyzed using ICDO. Results We have developed the ICDO to ontologize the ICD terms and relations. Different from existing disease ontologies, all ICD diseases in ICDO are defined as disease processes to describe their occurrence with other properties. The ICDO decomposes each disease term into different components, including anatomic entities, process profiles, etiological causes, output phenotype, etc. Over 900 ICD terms have been represented in ICDO. Many ICDO terms are presented in both English and Chinese. The ICD10/ICD9-based diagnosis data of over 27,000 COVID-19 patients from 5 countries were extracted from the 4CE. A total of 917 COVID-19-related disease codes, each of which were associated with 1 or more cases in the 4CE dataset, were mapped to ICDO and further analyzed using the ICDO logical annotations. Our study showed that COVID-19 targeted multiple systems and organs such as the lung, heart, and kidney. Different acute and chronic kidney phenotypes were identified. Some kidney diseases appeared to result from other diseases, such as diabetes. Some of the findings could only be easily found using ICDO instead of ICD9/10. Conclusions ICDO was developed to ontologize ICD10/10 codes and applied to study COVID-19 patient diagnosis data. Our findings showed that ICDO provides a semantic platform for more accurate detection of disease profiles.

Tese de Doutoramento em Ciências da Informação apresentada à Faculdade de Letras da Universidade de Coimbra. Uma organização do conhecimento compartimentado em disciplinas não se mostra adequada a uma práxis científica que carece de abertura interdisciplinar. Uma potencial solução envolve uma mudança do foco epistemológico para o ontológico na modulação de novos Sistemas de Organização do Conhecimento (SOC). Uma classificação ontológica, dos seres e não dos saberes, incidirá sobre o que, à partida, é comum aos vários domínios – a realidade. Nesta conjuntura é necessário distinguir como o conhecimento é adquirido e fundamentado da forma como a realidade é constituída e estruturada. Este ponto de partida envolve um debate milenar em torno da designada questão da cognição universal. Um posicionamento sustentado relativo a essa questão mostra-se necessário para o desenvolvimento consistente de SOC de cariz ontológico com o potencial para melhor servir a prática interdisciplinar. Pretende-se contribuir para a clarificação relativa à abordagem ontológica; contextualizando-a relativamente à cognição universal e aos conceitos, enquanto unidades de conhecimento dos SOC, complementando, empiricamente, o estudo com uma comparação entre as abordagens ontológicas dos SOC: Integrative Levels Classification (ILC) e Basic Formal Ontology (BFO). Assim, pretende-se: i. esquematizar os requisitos dum sistema ontológico; ii. apresentar um panorama geral relativo à questão da cognição universal; iii. identificar os principais modos de existência atribuídos aos conceitos e sua adequação a unidades de SOC; iv. caracterizar as abordagens ontológicas da ILC e da BFO; e v. sistematizar recomendações de auxílio à modelação de SOC de natureza ontológica. Para cumprir tais objetivos usaram-se os métodos analíticos hermenêutico-dialético e comparativo. Conclui-se que um SOC ontológico parte duma análise ontológica, i.e., uma categorização de tipos de entidades existentes que podem ser objetivamente subsumidos sob categorias distinguíveis. O sistema resultante poderá não definir rigorosamente as entidades que incorpora e as relações entre as mesmas, como acontece na ILC. Nesta procura-se explicitamente a integração da dimensão epistemológica sendo a naturalidade das suas classes expressa apenas implicitamente. Os conceitos continuam a ser entendidos como peças elementares dos SOC, com uma formulação muito próxima da de Dahlberg. Na BFO não só é explicitada a preocupação desta conter apenas classes naturais, como os conceitos são apenas entendidos como ferramentas cognitivas de acesso à realidade. O sistema, além de definições rigorosas, resulta de princípios ontológicos e formais rigorosos e uma adequação à realidade exterior ao próprio. Estas são as caraterísticas dum modelo ontológico que se distingue de sistemas com abordagens mais permissivas, derivada da não definição rigorosa dos seus constituintes ou da não aplicação da referida análise rigorosa de índole filosófica. No âmbito dos SOC, sobressaiu a inadequação da interpretação psicológica dos conceitos, atendendo ao cariz individual destes enquanto entidades mentais. Na perspetiva linguística os conceitos são comummente interpretados como etiquetas dos respetivos significados, sendo estes associados à leitura epistemológica. Nesta, se a sua abrangência não promover a separação entre objetos unicamente intencionais de outros de diferente natureza, será problemática a sua aplicação ao desenvolvimento de SOC ontológicos. Diferentes especificações relativas à natureza da realidade são impostas por distintos posicionamentos epistémicos. Foi possível verificar um mínimo denominador comum relativo a essa realidade. Sejam os posicionamentos considerados realistas, nominalistas ou conceitualistas, direta ou indiretamente, a sustentação das perceções humanas é colocada em algo independente das mesmas. Apesar das potenciais imperfeições das perceções humanas, elas não serão simplesmente, ou unicamente, sobre si mesmas. Neste contexto, são propostas cinco recomendações para auxílio à modulação de SOC ontológicos: i. apresentar definições precisas e ontologicamente consistentes das classes a incluir; ii. diferenciar claramente as classes das respetivas instâncias particulares; iii. classificar com base nos traços intrínsecos pertencentes às respetivas entidades; iv. não representar os dados de uma base em particular, mas os géneros de entidade existentes no domínio ao qual os dados reportam; v. usar as representações cognitivas como um meio para representar as entidades existentes no respetivo domínio e não como elemento a ser representado. Estas recomendações pretendem ser um ponto de partida para futuro desenvolvimento e não como a panaceia para a problemática envolvida. A presento tese não pretender ser uma resposta cabal nem definitiva à questão abordada, mas espera-se ter detalhe suficiente, para que, no contexto da Ciência da Informação, a abordagem ontológica seja melhor compreendida e encarada como uma alternativa viável à epistemológica.

The World Health Organization (WHO) indicates that the proportion of the elderly will soon include nearly a quarter of the world population. Ensuring that health systems are prepared to deal with this phenomenal rate of aging and associated diseases generates many challenges. Among these challenges is facing Alzheimer’s Disease (AD) that may occur at some point in the elderly life and may harm societies. AD is considered a neurological, psychological, mental, and health setback. The Clinical Decision Support System (CDSS) can improve patient care and support many medical functions, such as diagnosing diseases that can reduce preventable harm. This research’s main objective is to design, implement, and evaluate the Alzheimer’s Disease Diagnosis Ontology (ADDO). It is a comprehensive semantic knowledge base toward the development of fuzzy ontology-based CDSS for AD diagnosis. ADDO can serve as a core component of CDSS, which provides representation, annotation, and access to aspects related to AD’s study and diagnosis. Toward the management of this objective, ADDO is based on the essentials of the Open Biomedical Ontology (OBO) and follows the Basic Formal Ontology (BFO) and the Ontology for General Medical Sciences (OGMS) principles. ADDO focuses on representing the patient characteristics, complications, drugs, diagnosis examination tests, and key aspects of their periodic visits in a standard way. The possibility of semantic interoperability is taken into account by integrating ADDO and a heterogeneous AD dataset. We used ADNI as a case study to mapping a set of real instances. To manage the medical domain’s uncertainty, ADDO is extended to fuzzy ontology to accommodate the medical linguistic variables and enhance diagnosis results’ efficiency. ADDO is constructed using Protege 5.5.0 software and evaluated using the HermiT reasoner and SPARQL semantic queries. ADDO currently includes 7060 concepts, 99 properties, 46274 axioms, and 30708 annotations. As a result, ADDO is consistent and more reliable in managing most AD aspects than other existing AD ontologies.

Managing complex disaster situations is a challenging task because of the large number of actors involved and the critical nature of the events themselves. In particular, the different terminologies and technical vocabularies that are being exchanged among Emergency Responders (ERs) may lead to misunderstandings. Maintaining a shared semantics for exchanged data is a major challenge. To help to overcome these issues, we elaborate a modular suite of ontologies called POLARISCO that formalizes the complex knowledge of the ERs. Such a shared vocabulary resolves inconsistent terminologies and promotes semantic interoperability among ERs. In this work, we discuss developing POLARISCO as an extension of Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). We conclude by presenting a real use-case to check the efficiency and applicability of the proposed ontology.

We present a machine-readable movement writing for sleight-of-hand moves with cards -- a "Labanotation of card magic." This scheme of movement writing contains 440 categories of motion, and appears to taxonomize all card sleights that have appeared in over 1500 publications. The movement writing is axiomatized in $\mathcal{SROIQ}$(D) Description Logic, and collected formally as an Ontology of Card Sleights, a computational ontology that extends the Basic Formal Ontology and the Information Artifact Ontology. The Ontology of Card Sleights is implemented in OWL DL, a Description Logic fragment of the Web Ontology Language. While ontologies have historically been used to classify at a less granular level, the algorithmic nature of card tricks allows us to transcribe a performer's actions step by step. We conclude by discussing design criteria we have used to ensure the ontology can be accessed and modified with a simple click-and-drag interface. This may allow database searches and performance transcriptions by users with card magic knowledge, but no ontology background., Comment: 8 pages. Preprint. Final version appeared in ICSC 2019. Copyright of final version is held by IEEE

As engineering applications require management of ever larger volumes of data, ontologies offer the potential to capture, manage, and augment data with the capability for automated reasonin...

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy., Comment: 23 pages, 1 Table

Ontologies are being used in many areas of science to improve clarity and communication of research methods, findings and theories. Many of these ontologies use an upper level ontology called Basic Formal Ontology (BFO) as their frame of reference. This article summarises Basic Formal Ontology and shows how it can provide a basis for development of an Addiction Ontology that encompasses all the things that addiction researchers, practitioners and policy makers want to refer to. BFO makes a fundamental distinction between what it calls continuants (e.g. objects and their characteristics) and occurrents (e.g. processes). Classifying addiction-related entities using this system enables important distinctions to be made that are frequently overlooked or confused in the literature due to inherent ambiguities in natural language expressions. The Addiction Ontology uses this framework to convey information about: people and populations and their characteristics (e.g. substance use disorder), products (e.g. heroin, tobacco-containing products), behaviours (e.g. cigarette smoking, alcohol consumption), interventions (e.g. detoxification, rehabilitation, legislation), research (e.g. measurement, theories, study designs), organisations (e.g. pharmaceutical industry, tobacco companies), and settings (e.g. hospital outpatient clinic, country).

This paper describes how terminology and ontologies interact in a coherent and exhaustive manner, and how the neological appearance is obtained from the derivation of the specificity and functionality of a specific corpus. This research shows categories that do not exist either in the Glossary of Costa Rican popular cuisine (SEDÓ MASÍS, 2008), or in the Dictionary of Costarriquenismos (QUESADA PACHECO, 2012). The omission of some of these categories clearly shows that the terms have been historically organized taking into account only the lexical factor, but not the conceptual organization. Therefore, this study intends to play with the "disagreement" between speakers and specialists to show that a reorganization of the terms of an area is possible. For this, a corpus of 596 recipes and 4652 ingredients for ontological representation was compiled. Each ontological class was defined by means of its functionality in the recipes. The ontological architecture is formalized through the postulates of Basic Formal Ontology (SMITH, 2014) and is schematized by means of Protégé. In addition, the terminological theories of Cabré (1999), Fedor de Diego (1995) and Roche (2007, 2009) are used. En este trabajo se describe cómo interactúan, de forma coherente y exhaustiva, la terminología y las ontologías y de la que se obtiene la aparición neológica proveniente de la especificidad y funcionalidad de un corpus específico. Esta investigación muestra categorías que no existen ni en el Glosario de Cocina Popular Costarricense (SEDÓ MASÍS, 2008), ni en el Diccionario de Costarriqueñismos (QUESADA PACHECO, 2012). La omisión de algunas de estas categorías muestra claramente que los términos han sido organizados históricamente tomando en cuenta sólo el factor léxico, pero no la organización conceptual. Pues bien, este estudio intenta poner en juego el “desacuerdo” entre hablantes y especialistas para mostrar que es posible una reorganización de los términos de un área. Para esto se recopiló un corpus de 596 recetas y 4652 ingredientes para la representación ontológica. Cada clase ontológica se definió por medio de su funcionalidad en las recetas. La arquitectura ontológica se formaliza por medio de los postulados de Basic Formal Ontology (SMITH, 2014) y se esquematiza por medio de Protégé. Además, se emplean las teorías terminológicas de Cabré (1999), Fedor de Diego (1995) y Roche (2007, 2009). Este artigo descreve como terminologia e ontologias interagem de maneira coerente e exaustiva, e como a aparência neológica é obtida a partir da derivação da especificidade e funcionalidade de um corpus específico. A pesquisa mostra categorias que não existem nem no Glossário da Culinária Popular Costarriquenha (SEDÓ MASÍS, 2008), nem no Dicionário de Costa Rica (QUESADA PACHECO, 2012). A omissão de algumas dessas categorias mostra claramente que os termos foram historicamente organizados levando em consideração apenas o fator lexical, mas não a organização conceitual. O estudo tenta colocar em jogo a “discordância” entre palestrantes e especialistas para mostrar que uma reorganização dos termos de uma área é possível. Para isso, foi compilado um corpus de 596 receitas e 4652 ingredientes para representação ontológica. Cada classe ontológica foi definida por sua funcionalidade em receitas. A arquitetura ontológica foi formalizada através dos postulados da Ontologia Formal Básica (SMITH, 2014) e esquematizada através do Protégé. Além disso, foram utilizadas as teorias terminológicas de Cabré (1999), Fedor de Diego (1995) e Roche (2007, 2009).

Fundamentally, the engineering designs are collections of information, which contain the structural, qualitative and the behavioral (kinematic and static) specifications of some physical products that satisfy some customer requirements. Vast suits of information exchange standards, most notably ISO10303 (STEP), are currently being used for capturing product design in manufacturing industries. In recent years, a modern practice of capturing the product design information using model-based definition (MBD) (STEP AP242, ASME Y14.5) is becoming increasingly popular due to its ability to capture geometrical dimension and tolerances (GD&T) against managed models of 3D design features instead of traditional 2D drafting, facilitating communication of the design intents, and product model information (PMI). However, MBD suffers from difficulties in standardization of managed models due to diversity of views, stemming from cultural and traditional differences among the product designers and organizations. In the wake of semantic web technologies, researchers proposed number of semantic models for capturing product design concepts based on ontological taxonomy and rules, aiming to integrate heterogeneous sources of data based on contextual meaning of the information. In this article, we present an upper level ontology for capturing MBD based product designs, including axiomatic definitions of concepts related to product design (e.g. part, topology, feature, dimension, and tolerance). The primary goal of such semantic model is to increase the interoperability of MBD based product design and integrate domain-specific design features from manufacturability perspective (CNC, sheet metal, molding, 3D printing). In order to achieve such cross-domain interoperability, we based our design on Basic Formal Ontology (BFO). The applicability of the proposed ontology is elucidated by presenting usage pattern based on example design.

The promise of distributed cloud manufacturing (CM) is to manufacture products using shared resources, both asset-full (machines, tools, vehicles) and asset-light (design, analysis, inspection, management, maintenance), which can be provisioned flexibly and rapidly with minimal management and service provider interaction. One of the key enabler of CM are virtual enterprises (VE), which offer manufacturing resources as virtual services (SaaS, HaaS, PaaS) in a cloud based marketplace. Currently, virtualization and provisioning of diverse array of manufacturing resources face challenges from the heterogeneity in representation and communication protocols, as well as lack of integration with legacy practices in the organizations. Aiming to increase interoperability, a number of formal ontologies were developed by researchers in the past, to leverage on semantic data integration and validation. In spite of their success in providing axiomatic description and common taxonomy to classify manufacturing resources from different domains, models of representing the capabilities of the resources (i.e. expected quality of services they offer) were often overlooked. In this research, we present an ontology model to represent capabilities of manufacturing machine-tools at the process level, often called process boundaries (measured by process capability index) in industries. The definition of the capability is derived based on the foundational ontology called ‘Basic Formal Ontology’ (BFO). The primary contribution of this extension is a set of OWL axioms which can be used to assert facts about modal future (possibilia) – ultimately enabling us to associate process specific performance metrics to the semantic models of virtual machine-tools.

The goal of implementing an enterprise digital engineering strategy is to improve data sharing throughout system conceptualization, development, manufacturing, operations, sustainment and their supporting organizations. Data sharing is a critical element of interoperability of models and other digital artifacts supporting the systems engineering process. Achieving interoperability within systems engineering requires eliminating differences of syntax and semantics to take advantage of automation, augmentation, and artificial intelligence capabilities that will increase process efficiency and artifact quality. This paper describes the development of a process-centric Systems Engineering (SE) reference ontology, based upon ISO/IEC/IEEE 15288 Systems and Software Engineering – Life Cycle Processes [1]. The proposed SE reference ontology extends from the Basic Formal Ontology (BFO), which is a very small top-level ontology (TLO) designed specifically to support the integration of more specific ontologies that extend from it [2]. A reference ontology is intended to provide a comprehensive representation of the entities in a given domain encapsulating the terminological content of established knowledge. Unlike other systems ontologies, which focus on the target systems, this reference ontology is focused on the systems engineering process, providing context and purpose for the digital artifacts being built and linking the content of artifacts together into one lexicon.

Purpose This study aims to explore a way of representing historical collections by examining the features of an event in historical documents and building an event-based ontology model. Design/methodology/approach To align with a domain-specific and upper ontology, the Basic Formal Ontology (BFO) model is adopted. Based on BFO, an event-based ontology for historical description (EOHD) is designed. To define events, event-related vocabularies are taken from the Library of Congress’ event types (2012). The three types of history and six kinds of changes are defined. Findings The EOHD model demonstrates how to apply the event ontology to biographical sketches of a creator history to link event types. Research limitations/implications The EOHD model has great potential to be further expanded to specific events and entities through different types of history in a full set of historical documents. Originality/value The EOHD provides a framework for modeling and semantically reforming the relationships of historical documents, which can make historical collections more explicitly connected in Web environments.

Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is a comprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic health data. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but these efforts have been hampered by the size and complexity of SCT. Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the terms in SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks of definitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-level SCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. The approach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundry ontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-level ontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555 annotations. It is publicly available through the bioportal at . The resulting ontology can enhance the semantics of clinical decision support systems and semantic interoperability among distributed electronic health records. In addition, the populated ontology can be used for the automation of mobile health applications.

A conceptual model abstractly defines a number of concepts and their relationships for the purposes of understanding and communication. Once a conceptual model is available, it can also be used as a starting point for the development of a software system. The development of conceptual models using the Unified Modeling Language (UML) facilitates the representation of modeled concepts and allows software developers to directly reuse these concepts in the design of a software system. The OBO Foundry represents the most relevant collaborative effort towards the development of ontologies in the biomedical domain. The development of UML conceptual models in the biomedical domain may benefit from the use of domain-specific semantics and notation. Further, the development of these models may also benefit from the reuse of knowledge contained in OBO ontologies. This paper investigates the support for the development of conceptual models in the biomedical domain using UML as a conceptual modeling language and using the support provided by the OBO Foundry for the development of biomedical ontologies, namely entity kind and relationship types definitions provided by the Basic Formal Ontology (BFO) and the OBO Core Relations Ontology (OBO Core), respectively. Further, the paper investigates the support for the reuse of biomedical knowledge currently available in OBOFFF ontologies in the development these conceptual models. The paper describes a UML profile for the OBO Core Relations Ontology, which basically defines a number of stereotypes to represent BFO entity kinds and OBO Core relationship types definitions. The paper also presents a support toolset consisting of a graphical editor named OBO-RO Editor, which directly supports the development of UML models using the extensions defined by our profile, and a command-line tool named OBO2UML, which directly converts an OBOFFF ontology into a UML model.

Background Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge. Results This paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients’ current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology’s BioPortal at http://bioportal.bioontology.org/ontologies/DMTO. The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs. Conclusion The ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems. Electronic supplementary material The online version of this article (10.1186/s13326-018-0176-y) contains supplementary material, which is available to authorized users.