Editorial: Formal Ontologies meet Industry

The Formal Ontologies meet Industry (FOMI) workshop series is a scientific initiative supported by the International Association for Ontology and its Applications (IAOA) aimed at bringing together academics and practitioners interested in ontologies for industry. FOMI addresses research and application topics concerning, e.g., the design of domain-specific ontologies, the development of ontology-based information systems, or the investigation of the theoretical underpinnings of formal ontology when tuned to engineering applications

Redesign Support Framework for Complex Technical Processes

Els processos industrials requereixen avaluacions periòdiques per a verificar la seva correcta operació en termes tècnics i econòmics. Aquestes avaluacions són necessàries a causa de els canvis en els mercats i en la legislació ambiental i de seguretat. Per a satisfer aquestes demandes és necessari investigar les alternatives dels processos que permetin l'ús òptim dels recursos existents amb la mínima inversió econòmica possible. Aquesta tasca es coneix com redisseny, que és un procediment per a determinar possibles canvis en un procés existent per a millorar-lo pel en alguna mètrica, tal com econòmica, ambiental, de seguretat, etc. En aquesta tesi es proposa un marc d'ajuda al redisseny per a processos tècnics. Aquest marc fa ús d'una representació jeràrquica de models múltiples del procés que es re dissenyarà en conjunció amb un motor que raonament basat en casos per a ajudar a decidir quins elements del procés han de ser modificats. El marc consisteix en quatre etapes principals: adquisició de la descripció del disseny, identificació de candidats, generació d'alternatives, i adaptació i avaluació d'alternatives.El procés original es modela jeràrquicament emprant conceptes de mitjans-fins i parts-tot. Així el coneixement sobre el comportament, l'estructura, la funció i l'objectiu de cadascuna de les parts del procés es genera i s'emmagatzema automàticament. Donat les noves especificacions o requisits que el procés ha de satisfer, el sistema troba les parts del procés que ha de ser redissenyades. S'utilitza una llibreria de casos per a obtenir seccions alternatives del procés que es puguin adaptar per a substituir parts del procés original. Per tant, el marc proposat permet modelar el procés, identificar els components de procés viables a redissenyar, obtenir components alternatius i finalment adaptar aquests components alternatius en el procés original. Aquest procediment es pot veure com activitat d'enginyeria inversa on es generen models abstractes en diversos nivells a partir d'una descripció detallada d'un procés existent per a reduir la seva complexitat. El marc ha estat implementat i provat en el domini d'Enginyeria Química.Los procesos industriales requieren evaluaciones periódicas para verificar su correcta operación en términos técnicos y económicos. Estas evaluaciones son necesarias debido a los cambios en los mercados y en la legislación ambiental y de seguridad. Para satisfacer estas demandas es necesario investigar las alternativas de los procesos que permitan el uso óptimo de los recursos existentes con la mínima inversión económica posible. Esta tarea se conoce como rediseño, que es un procedimiento para determinar posibles cambios en un proceso existente para mejorarlo con respecto a alguna métrica, tal como económica, ambiental, de seguridad, etc.En esta tesis se propone un marco de ayuda al rediseño para procesos técnicos. Este marco emplea una representación jerárquica de modelos múltiples del proceso que se rediseñará en conjunción con un motor que razonamiento basado en casos para ayudar a decidir qué elementos del proceso deben ser modificados. El marco consiste en cuatro etapas principales: adquisición de la descripción del diseño, identificación de candidatos, generación de alternativas, y adaptación y evaluación de alternativas. El proceso original se modela jerárquicamente empleando conceptos de medios-fines y partes-todo. Así el conocimiento sobre el comportamiento, la estructura, la función y el objetivo de cada una de las parte del proceso se genera y se almacena automáticamente. Dado las nuevas especificaciones o requisitos que el proceso debe satisfacer, el sistema encuentra las partes del proceso que debe ser rediseñadas. Se utiliza una librería de casos para obtener secciones alternativas del proceso que se puedan adaptar para sustituir partes del proceso original. Por lo tanto, el marco propuesto permite modelar el proceso, identificar los componentes de proceso viables a rediseñar, obtener componentes alternativos y finalmente adaptar estos componentes alternativos en el proceso original. Este procedimiento se puede ver como actividad de ingeniería inversa donde se generan modelos abstractos en diversos niveles a partir de una descripción detallada de un proceso existente para reducir su complejidad. El marco ha sido implementado y probado en el dominio de Ingeniería Química.Industrial processes require periodic evaluations to verify their correct operation, both in technical and economical terms. These evaluations are necessary due to changes in the markets, and in safety and environmental legislation. In order to satisfy these demands it is necessary to investigate process alternatives that allow the optimal use of existing resources with the minimum possible investment. This task is known as redesign, which is a procedure to determine possible changes to an existing process in order to improve it with respect to some metric, such as economical, environmental, safety, etc.A redesign support framework for technical processes is proposed in this thesis. This framework employs a multiple-model hierarchical representation of the process to be redesigned together with a case-based reasoning engine that helps to decide which elements of the process should be modified. The framework consists of four main stages: acquisition of the design description, identification of candidates, generation of alternatives, and adaptation and evaluation of alternatives.The original process is modelled hierarchically exploiting means-end and part-whole concepts, and thus knowledge about the behaviour, structure, function and intention of each part of the process is automatically generated and stored. Given the new specifications or requirements that the process must fulfil, the system finds the parts of the process which must be redesigned and a case library is used to obtain alternative process sections which can be adapted to substitute parts of the original process. Therefore, the proposed framework allows to model the process, to identify process components suitable for redesign, to obtain alternative components, and finally, to adapt these components into the original process. This procedure can be seen as a reverse engineering activity where abstract models at different levels are generated from a detailed description of an existing process to reduce its complexity. The framework has been implemented and tested on the Chemical Engineering domain.Postprint (published version

A Step Toward Improving Healthcare Information Integration & Decision Support: Ontology, Sustainability and Resilience

The healthcare industry is a complex system with numerous stakeholders, including patients, providers, insurers, and government agencies. To improve healthcare quality and population well-being, there is a growing need to leverage data and IT (Information Technology) to support better decision-making. Healthcare information systems (HIS) are developed to store, process, and disseminate healthcare data. One of the main challenges with HIS is effectively managing the large amounts of data to support decision-making. This requires integrating data from disparate sources, such as electronic health records, clinical trials, and research databases. Ontology is one approach to address this challenge. However, understanding ontology in the healthcare domain is complex and difficult. Another challenge is to use HIS on scheduling and resource allocation in a sustainable and resilient way that meets multiple conflicting objectives. This is especially important in times of crisis when demand for resources may be high, and supply may be limited. This research thesis aims to explore ontology theory and develop a methodology for constructing HIS that can effectively support better decision-making in terms of scheduling and resource allocation while considering system resiliency and social sustainability. The objectives of the thesis are: (1) studying the theory of ontology in healthcare data and developing a deep model for constructing HIS; (2) advancing our understanding of healthcare system resiliency and social sustainability; (3) developing a methodology for scheduling with multi-objectives; and (4) developing a methodology for resource allocation with multi-objectives. The following conclusions can be drawn from the research results: (1) A data model for rich semantics and easy data integration can be created with a clearer definition of the scope and applicability of ontology; (2) A healthcare system's resilience and sustainability can be significantly increased by the suggested design principles; (3) Through careful consideration of both efficiency and patients' experiences and a novel optimization algorithm, a scheduling problem can be made more patient-accessible; (4) A systematic approach to evaluating efficiency, sustainability, and resilience enables the simultaneous optimization of all three criteria at the system design stage, leading to more efficient distributions of resources and locations for healthcare facilities. The contributions of the thesis can be summarized as follows. Scientifically, this thesis work has expanded our knowledge of ontology and data modelling, as well as our comprehension of the healthcare system's resilience and sustainability. Technologically or methodologically, the work has advanced the state of knowledge for system modelling and decision-making. Overall, this thesis examines the characteristics of healthcare systems from a system viewpoint. Three ideas in this thesis—the ontology-based data modelling approach, multi-objective optimization models, and the algorithms for solving the models—can be adapted and used to affect different aspects of disparate systems

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available