Semantic interoperability: ontological unpacking of a viral conceptual model

Background. Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. Results. In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the “ontological unpacking” method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. Conclusions. We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the “I” in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research

Suporte automatizado para desenvolvimento de ontologias utilizando padrões ontológicos de domínio

A Engenharia de Ontologias tem evoluído bastante nas últimas décadas, com um número crescente de metodologias, ferramentas e aplicativos, que estão sendo propostos e experimentados na academia e na indústria. Por meio das ontologias, o conhecimento compartilhado de um domínio pode ser modelado para ser comunicado entre pessoas e sistemas automatizados. Com isso, a utilização de ontologias se torna uma importante ferramenta em diversas áreas do conhecimento para se estruturar, organizar e apoiar o compartilhamento dos conceitos que são inerentes a essas áreas. Além disso, com o uso de ontologias, a interoperabilidade entre sistemas se torna possível, devido à normatização e ao uso de padrões em sua construção. No entanto, o desenvolvimento de ontologias a partir do zero é uma tarefa difícil e complexa, uma vez que uma ontologia deve fornecer uma representação completa e coerente de uma parte específica do mundo. Assim, a reutilização é altamente recomendada em seu desenvolvimento, permitindo que as ontologias sejam construídas com base em modelos pré-existentes, levando a melhores resultados quanto a sua qualidade. Neste sentido, Padrões Ontológicos (OPs) são considerados como ferramentas interessantes para facilitar a reutilização. Recentemente, vários autores da comunidade de Engenharia de Ontologias já propuseram OPs e mecanismos para aplicá-los. No entanto, sistemas automatizados para apoiar a sua utilização na prática ainda são raros. Para preencher esta lacuna, esta dissertação propõe um editor para catálogos OPs, cujo objetivo é apoiar o gerenciamento e o reúso desses padrões. Assim, a abordagem de catálogo de OPs pode ser aplicada na construção de ontologias, com suporte automático. No desenvolvimento do editor proposto, optou-se por estender um editor de ontologias existente (o OLED) para aproveitar suas ferramentas de modelagem, verificação, transformação e validação. Também foram parcialmente implementados três catálogos de OPs específicos para os domínios de Serviço, de Processo de Software baseado na ISO e de Colaboração. Além disso, esta dissertação descreve três exemplos de utilização, um para cada um dos domínios citados, visando demonstrar a viabilidade da abordagem de construção de ontologias utilizando catálogos de OPs com o uso do editor desenvolvido, enfatizando também o benefício do reúso de OPs

Representation of Multi-Level Domains on The Web

Estratégias de modelagem conceitual e representação de conhecimento frequentemente tratam entidades em dois níveis: um nível de classes e um nível de indivíduos que instanciam essas classes. Em vários domínios, porém, as próprias classes podem estar sujeitas a categorização, resultando em classes de classes (ou metaclasses). Ao representar estes domínios, é preciso capturar não apenas as entidades de diferentes níveis de classificação, mas também as suas relações (possivelmente complexas). No domínio de taxonomias biológicas, por exemplo, um dado organismo (por exemplo, o leão Cecil morto em 2015 no Parque Nacional Hwange no Zimbábue) é classificado em diversos táxons (como, por exemplo, Animal, Mamífero, Carnívoro, Leão), e cada um desses táxons é classificado por um ranking taxonômico (por exemplo, Reino, Classe, Ordem, Espécie). Assim, para representar o conhecimento referente a esse domínio, é necessário representar entidades em níveis diferentes de classificação. Por exemplo, Cecil é uma instância de Leão, que é uma instância de Espécie. Espécie, por sua vez, é uma instância de Ranking Taxonômico. Além disso, quando representamos esses domínios, é preciso capturar não somente as entidades diferentes níveis de classificação, mas também suas (possivelmente complicadas) relações. Por exemplo, nós gostaríamos de afirmar que instâncias do gênero Panthera também devem ser instâncias de exatamente uma instância de Espécie (por exemplo, Leão). A necessidade de suporte à representação de domínios que lidam com múltiplos níveis de classificação deu origem a uma área de investigação chamada modelagem multi-nível. Observa-se que a representação de modelos com múltiplos níveis é um desafio em linguagens atuais da Web Semântica, como há pouco apoio para orientar o modelador na produção correta de ontologias multi-nível, especialmente por causa das nuanças de restrições que se aplicam a entidades de diferentes níveis de classificação e suas relações. A fim de lidar com esses desafios de representação, definimos um vocabulário que pode ser usado como base para a definição de ontologias multi-nível em OWL, juntamente com restrições de integridade e regras de derivação. É oferecida uma ferramenta que recebe como entrada um modelo de domínio, verifica conformidade com as restrições de integridade propostas e produz como saída um modelo enriquecido com informações derivadas. Neste processo, é empregada uma teoria axiomática chamada MLT (uma Teoria de Modelagem Multi-Nível). O conteúdo da plataforma Wikidata foi utilizado para demonstrar que o vocabulário poderia evitar inconsistências na representação multi-nível em um cenário real

Ontology Validation for Managers

Ontology driven conceptual modeling focuses on accurately representing a domain of interest, instead of making information fit an arbitrary set of constructs. It may be used for different purposes, like to achieve semantic interoperability (Nardi, Falbo and Almeida, 2013), development of knowledge representation models (Guizzardi and Zamborlini, 2012) and language evaluation (Santos, Almeida and Guizzardi,2010). Regardless its final application, a model must be accurately defined in order for it to be a successful solution. This new branch of conceptual modeling improves traditional techniques by taking into consideration ontological properties, such as rigidity, identity and dependence, which are derived from a foundational ontology. This increasing interest in more expressive languages for conceptual modeling is shown by OMGs request for language proposals for the Semantic Information Model Federation (SIMF) (OMG,2011). OntoUML (Guizzardi, 2005) is an example of a language designed for that purpose.Its metamodel (Carraretto, 2010) is designed to comply to the Unified Foundational Ontology (UFO). It focus on structural aspects of individuals and universals.Grounded on human cognition and linguistics, it aims to provide the most basic categories in which humans understand and classify things around them.In (Guizzardi, 2010) Guizzardi quotes the famous Dijkstras lecture, in which he discusses the humble programmer and makes an analogy entitled the humble ontologist. He argues that the task of ontology-driven conceptual modeling is extremely complex and thus, modelers should surround themselves with as many tools as possible to aid in the development of the ontology. These complexities arise from different sources. A couple of them come from foundational ontology itself, both its modal nature, which imposes modelers to deal with possibilities, and the many different restrictions of each ontological category. But they also come from the need of accurately defining instance level constraints, which require additional rules, outside of the languages graphical notation. To help modelers to develop high quality OntoUML models, a number of tools have been proposed to aid in different phases of conceptual modeling. From the construction of the models themselves using design patterns questions (Guizzardi et al., 2011), to automatic syntax verification (Benevides, 2010) and model validation through simulation (Benevides et al., 2010). The importance of domain specification that accurately captures the intended conceptualization has been recognized by both the traditional conceptual modeling community (Moody et al., 2003) and the ontology community (Vrandečić, 2009). In this research we want to improve (Benevides et al., 2010) initiative, but focus exclusively on the validation of ontology driven conceptual models, and not on verification. With the complexity of the modeling activity in mind, we want to help modelers to systematically produce high quality ontologies, improving precision and coverage (Gangemi et al., 2005) of the models. We intend to make the simulationbased approach available for users that are not experts in the formal method, relieving them of the need to learn yet another language, solely for the purpose of validating their models

Representing Dynamic Invariants in Ontologically Well-Founded Conceptual Models

Conceptual models often capture the invariant aspects of the phenomena we perceive. These invariants may be considered static when they refer to structures we perceive in phenomena at a particular point in time or dynamic/temporal when they refer to regularities across different points in time. While static invariants have received significant attention, dynamics enjoy marginal support in widely-employed techniques such as UML and OCL. This thesis aims at addressing this gap by proposing a technique for the representation of dynamic invariants of subject domains in UML-based conceptual models. For that purpose, a temporal extension of OCL is proposed. It enriches the ontologically well-founded OntoUML profile and enables the expression of a variety of (arbitrary) temporal constraints. The extension is fully implemented in the tool for specification, verification and simulation of enriched OntoUML models

Knowledge Representation in Engineering 4.0

This dissertation was developed in the context of the BMBF and EU/ECSEL funded projects GENIAL! and Arrowhead Tools. In these projects the chair examines methods of specifications and cooperations in the automotive value chain from OEM-Tier1-Tier2. Goal of the projects is to improve communication and collaborative planning, especially in early development stages. Besides SysML, the use of agreed vocabularies and on- tologies for modeling requirements, overall context, variants, and many other items, is targeted. This thesis proposes a web database, where data from the collaborative requirements elicitation is combined with an ontology-based approach that uses reasoning capabilities. For this purpose, state-of-the-art ontologies have been investigated and integrated that entail domains like hardware/software, roadmapping, IoT, context, innovation and oth- ers. New ontologies have been designed like a HW / SW allocation ontology and a domain-specific "eFuse ontology" as well as some prototypes. The result is a modular ontology suite and the GENIAL! Basic Ontology that allows us to model automotive and microelectronic functions, components, properties and dependencies based on the ISO26262 standard among these elements. Furthermore, context knowledge that influences design decisions such as future trends in legislation, society, environment, etc. is included. These knowledge bases are integrated in a novel tool that allows for collabo- rative innovation planning and requirements communication along the automotive value chain. To start off the work of the project, an architecture and prototype tool was developed. Designing ontologies and knowing how to use them proved to be a non-trivial task, requiring a lot of context and background knowledge. Some of this background knowledge has been selected for presentation and was utilized either in designing models or for later immersion. Examples are basic foundations like design guidelines for ontologies, ontology categories and a continuum of expressiveness of languages and advanced content like multi-level theory, foundational ontologies and reasoning. Finally, at the end, we demonstrate the overall framework, and show the ontology with reasoning, database and APPEL/SysMD (AGILA ProPErty and Dependency Descrip- tion Language / System MarkDown) and constraints of the hardware / software knowledge base. There, by example, we explore and solve roadmap constraints that are coupled with a car model through a constraint solver.Diese Dissertation wurde im Kontext des von BMBF und EU / ECSEL gefördertem Projektes GENIAL! und Arrowhead Tools entwickelt. In diesen Projekten untersucht der Lehrstuhl Methoden zur Spezifikationen und Kooperation in der Automotive Wertschöp- fungskette, von OEM zu Tier1 und Tier2. Ziel der Arbeit ist es die Kommunikation und gemeinsame Planung, speziell in den frühen Entwicklungsphasen zu verbessern. Neben SysML ist die Benutzung von vereinbarten Vokabularen und Ontologien in der Modellierung von Requirements, des Gesamtkontextes, Varianten und vielen anderen Elementen angezielt. Ontologien sind dabei eine Möglichkeit, um das Vermeiden von Missverständnissen und Fehlplanungen zu unterstützen. Dieser Ansatz schlägt eine Web- datenbank vor, wobei Ontologien das Teilen von Wissen und das logische Schlussfolgern von implizitem Wissen und Regeln unterstützen. Diese Arbeit beschreibt Ontologien für die Domäne des Engineering 4.0, oder spezifischer, für die Domäne, die für das deutsche Projekt GENIAL! benötigt wurde. Dies betrifft Domänen, wie Hardware und Software, Roadmapping, Kontext, Innovation, IoT und andere. Neue Ontologien wurden entworfen, wie beispielsweise die Hardware-Software Allokations-Ontologie und eine domänen-spezifische "eFuse Ontologie". Das Ergebnis war eine modulare Ontologie-Bibliothek mit der GENIAL! Basic Ontology, die es erlaubt, automotive und mikroelektronische Komponenten, Funktionen, Eigenschaften und deren Abhängigkeiten basierend auf dem ISO26262 Standard zu entwerfen. Des weiteren ist Kontextwissen, welches Entwurfsentscheidungen beinflusst, inkludiert. Diese Wissensbasen sind in einem neuartigen Tool integriert, dass es ermöglicht, Roadmapwissen und Anforderungen durch die Automobil- Wertschöpfungskette hinweg auszutauschen. On tologien zu entwerfen und zu wissen, wie man diese benutzt, war dabei keine triviale Aufgabe und benötigte viel Hintergrund- und Kontextwissen. Ausgewählte Grundlagen hierfür sind Richtlinien, wie man Ontologien entwirft, Ontologiekategorien, sowie das Spektrum an Sprachen und Formen von Wissensrepresentationen. Des weiteren sind fort- geschrittene Methoden erläutert, z.B wie man mit Ontologien Schlußfolgerungen trifft. Am Schluss wird das Overall Framework demonstriert, und die Ontologie mit Reason- ing, Datenbank und APPEL/SysMD (AGILA ProPErty and Dependency Description Language / System MarkDown) und Constraints der Hardware / Software Wissensbasis gezeigt. Dabei werden exemplarisch Roadmap Constraints mit dem Automodell verbunden und durch den Constraint Solver gelöst und exploriert

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)