gOntt, a Tool for Scheduling and Executing Ontology Development Projects

Nowadays the ontology engineering field does not have any method that guides ontology practitioners when planning and scheduling their ontology development projects. The field also lacks the tools that help ontology practitioners to plan, schedule, and execute such projects. This paper tries to contribute to the solution of these problems by proposing the identification of two ontology life cycle models, the definition of the methodological basis for scheduling ontology projects, and a tool called gOntt that (1) supports the scheduling of ontology developments and (2) helps to execute such development projects

gOntt: a Tool for Scheduling Ontology Development Projects

The Ontology Engineering field lacks tools that guide ontology developers to plan and schedule their ontology development projects. gOntt helps ontology developers in two ways: (a) to schedule ontology projects; and (b) to execute such projects based on the schedule and using the NeOn Methodology

NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse

A new ontology development paradigm has started; its emphasis lies on the reuse and possible subsequent reengineering of knowledge resources, on the collaborative and argumentative ontology development, and on the building of ontology networks; this new trend is the opposite of building new ontologies from scratch. To help ontology developers in this new paradigm, it is important to provide strong methodological support. This thesis presents some contributions to the methodological area of the Ontology Engineering field that we are sure will improve the development and building of ontologies networks, and thus, - It proposes the NeOn Glossary of Processes and Activities, which identifies and defines the processes and activities potentially involved when ontology networks are collaboratively built. - It defines a set of two ontology network life cycle models. - It identifies and describes a collection of nine scenarios for building ontology networks. - It provides some methodological guidelines for performing the ontology requirements specification activity, to obtain the requirements that the ontology should fulfil. - It offers some methodological guidelines for obtaining the ontology network life cycle for a concrete ontology network, as part of scheduling ontology projects. Additionally, the thesis provides the technological support to these guidelines: a tool called gOntt. - It also proposes some methodological guidelines for the reuse of ontological resources at two different levels of granularity: as a whole (general ontologies and domain ontologies) and using ontology statements

Essentials In Ontology Engineering: Methodologies, Languages, And Tools

In the beginning of the 90s, ontology development was similar to an art: ontology developers did not have clear guidelines on how to build ontologies but only some design criteria to be followed. Work on principles, methods and methodologies, together with supporting technologies and languages, made ontology development become an engineering discipline, the so-called Ontology Engineering. Ontology Engineering refers to the set of activities that concern the ontology development process and the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. Thanks to the work done in the Ontology Engineering field, the development of ontologies within and between teams has increased and improved, as well as the possibility of reusing ontologies in other developments and in final applications. Currently, ontologies are widely used in (a) Knowledge Engineering, Artificial Intelligence and Computer Science, (b) applications related to knowledge management, natural language processing, e-commerce, intelligent information integration, information retrieval, database design and integration, bio-informatics, education, and (c) the Semantic Web, the Semantic Grid, and the Linked Data initiative. In this paper, we provide an overview of Ontology Engineering, mentioning the most outstanding and used methodologies, languages, and tools for building ontologies. In addition, we include some words on how all these elements can be used in the Linked Data initiative

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Desarrollo de una ontología para la publicación de datos de Plenos Municipales como herramienta hacia la creación de un estándar abierto

[ES] La participación ciudadana en asuntos políticos ha ido generando impacto y se ha convertido en un componente esencial para la toma de decisiones. Uno de los órganos de gobierno donde se toma gran parte de las decisiones que afecta la vida de las ciudadanas y ciudadanos es el Pleno. Sin embargo, la participación de la ciudadanía en este órgano, está limitada a la presentación de ruegos, reclamos o sugerencias. Por este motivo, la Cátedra de Gobierno Abierto de la Universidad Politécnica de València, se ha propuesto la definición de un estándar de información abierto que permita a la ciudadanía acceder a la información generada en los Plenos y participar activamente en todo el proceso de toma de decisiones a nivel municipal. Es por esto que, este trabajo de fin de máster surge de la necesidad de crear una herramienta de acceso abierto como parte del estándar, que defina un vocabulario de datos formal para publicar información plenaria, permitiendo a la ciudadanía acceder a ella y por ende fomentar la participación ciudadana. Para obtener el resultado deseado, se ha realizado el estudio del funcionamiento de las sesiones del Pleno y se ha realizado una encuesta a municipios españoles en el marco del Grupo de Trabajo para la Transparencia de Órganos Colegiados. Como resultado se obtiene la Ontología de Plenos Municipales de España, misma que, por su naturaleza, permitirá que los datos sean reutilizables, interoperables y entendibles por los ordenadores[EN] Citizen participation in politics has risen and generated a big impact, becoming essential for decision making. Plenary sessions are the main organisms in each city council where decisions affecting citizen’s lives are made. Nevertheless, citizen participation in this organism is limited to present petitions, questions or suggestions. For this reason, the Open Government Professorship of the Polytechnic University of València has proposed the definition of an open standard in order to allow citizens to access to the information of the plenary sessions, which helps them to participate actively in the whole process of decision making at a municipality level. This master thesis arises from the need of an open access tool, which is created as an essential part of the standard, and defines a data vocabulary to open plenary information, allowing citizens to access and therefore to promote citizen’s participation. The development of the vocabulary starts with the study of the functioning of a plenary session and the application of a survey to some municipalities, this survey has been proposed as part of the Working Group for Transparency of Collegiate Bodies. As result, we have obtained the Open City Council Ontology for Spain Municipalities, which, by its nature, will provide reusable, interoperable and machine readable data.Abad Regalado, KA. (2018). Desarrollo de una ontología para la publicación de datos de Plenos Municipales como herramienta hacia la creación de un estándar abierto. http://hdl.handle.net/10251/111904TFG

Developing Ontological Background Knowledge for Biomedicine

Biomedicine is an impressively fast developing, interdisciplinary field of research. To control the growing volumes of biomedical data, ontologies are increasingly used as common organization structures. Biomedical ontologies describe domain knowledge in a formal, computationally accessible way. They serve as controlled vocabularies and background knowledge in applications dealing with the integration, analysis and retrieval of heterogeneous types of data. The development of biomedical ontologies, however, is hampered by specific challenges. They include the lack of quality standards, resulting in very heterogeneous resources, and the decentralized development of biomedical ontologies, causing the increasing fragmentation of domain knowledge across them. In the first part of this thesis, a life cycle model for biomedical ontologies is developed, which is intended to cope with these challenges. It comprises the stages "requirements analysis", "design and implementation", "evaluation", "documentation and release" and "maintenance". For each stage, associated subtasks and activities are specified. To promote quality standards for biomedical ontology development, an emphasis is set on the evaluation stage. As part of it, comprehensive evaluation procedures are specified, which allow to assess the quality of ontologies on various levels. To tackle the issue of knowledge fragmentation, the life cycle model is extended to also cover ontology alignments. Ontology alignments specify mappings between related elements of different ontologies. By making potential overlaps and similarities between ontologies explicit, they support the integration of ontologies and help reduce the fragmentation of knowledge. In the second part of this thesis, the life cycle model for biomedical ontologies and alignments is validated by means of five case studies. As a result, they confirm that the model is effective. Four of the case studies demonstrate that it is able to support the development of useful new ontologies and alignments. The latter facilitate novel natural language processing and bioinformatics applications, and in one case constitute the basis of a task of the "BioNLP shared task 2013", an international challenge on biomedical information extraction. The fifth case study shows that the presented evaluation procedures are an effective means to check and improve the quality of ontology alignments. Hence, they support the crucial task of quality assurance of alignments, which are themselves increasingly used as reference standards in evaluations of automatic ontology alignment systems. Both, the presented life cycle model and the ontologies and alignments that have resulted from its validation improve information and knowledge management in biomedicine and thus promote biomedical research

Una propuesta de modelado del estudiante basada en ontologías y diagnóstico pedagógico-cognitivo no monótono

Los recientes avances tecnológicos han encontrado un potencial campo de explotación en la educación asistida por computador. A finales de los años 90 surgió un nuevo campo de investigación denominado Entornos Virtuales Inteligentes para el Entrenamiento y/o Enseñanza (EVIEs), que combinan dos áreas de gran complejidad: Los Entornos Virtuales (EVs) y los Sistemas de Tutoría Inteligente (STIs). De este modo, los beneficios de los entornos 3D (simulación de entornos de alto riesgo o entornos de difícil uso, etc.) pueden combinarse con aquéllos de un STIs (personalización de materias y presentaciones, adaptación de la estrategia de tutoría a las necesidades del estudiante, etc.) para proporcionar soluciones educativas/de entrenamiento con valores añadidos. El Modelo del Estudiante, núcleo de un SIT, representa el conocimiento y características del estudiante, y refleja el proceso de razonamiento del estudiante. Su complejidad es incluso superior cuando los STIs se aplican a EVs porque las nuevas posibilidades de interacción proporcionadas por estos entornos deben considerarse como nuevos elementos de información clave para el modelado del estudiante, incidiendo en todo el proceso educativo: el camino seguido por el estudiante durante su navegación a través de escenarios 3D; el comportamiento no verbal tal como la dirección de la mirada; nuevos tipos de pistas e instrucciones que el módulo de tutoría puede proporcionar al estudiante; nuevos tipos de preguntas que el estudiante puede formular, etc. Por consiguiente, es necesario que la estructura de los STIs, embebida en el EVIE, se enriquezca con estos aspectos, mientras mantiene una estructura clara, estructurada, y bien definida. La mayoría de las aproximaciones al Modelo del Estudiante en STIs y en IVETs no consideran una taxonomía de posibles conocimientos acerca del estudiante suficientemente completa. Además, la mayoría de ellas sólo tienen validez en ciertos dominios o es difícil su adaptación a diferentes STIs. Para vencer estas limitaciones, hemos propuesto, en el marco de esta tesis doctoral, un nuevo mecanismo de Modelado del Estudiante basado en la Ingeniería Ontológica e inspirado en principios pedagógicos, con un modelo de datos sobre el estudiante amplio y flexible que facilita su adaptación y extensión para diferentes STIs y aplicaciones de aprendizaje, además de un método de diagnóstico con capacidades de razonamiento no monótono. El método de diagnóstico es capaz de inferir el estado de los objetivos de aprendizaje contenidos en el SIT y, a partir de él, el estado de los conocimientos del estudiante durante su proceso de aprendizaje. La aproximación almodelado del estudiante propuesta ha sido implementada e integrada en un agente software (el agente de modelado del estudiante) dentro de una plataforma software existente para el desarrollo de EVIEs denominadaMAEVIF. Esta plataforma ha sido diseñada para ser fácilmente configurable para diferentes aplicaciones de aprendizaje. El modelado del estudiante presentado ha sido implementado e instanciado para dos tipos de entornos de aprendizaje: uno para aprendizaje del uso de interfaces gráficas de usuario en una aplicación software y para un Entorno Virtual para entrenamiento procedimental. Además, se ha desarrollado una metodología para guiar en la aplicación del esta aproximación de modelado del estudiante a cada sistema concreto.---ABSTRACT---Recent technological advances have found a potential field of exploitation in computeraided education. At the end of the 90’s a new research field emerged, the so-called Intelligent Virtual Environments for Training and/or Education (IVETs), which combines two areas of great complexity: Virtual Environments (VE) and Intelligent Tutoring Systems (ITS). In this way, the benefits of 3D environments (simulation of high risk or difficult-to-use environments, etc.) may be combined with those of an ITS (content and presentation customization, adaptation of the tutoring strategy to the student requirements, etc.) in order to provide added value educational/training solutions. The StudentModel, core of an ITS, represents the student’s knowledge and characteristics, and reflects the student’s reasoning process. Its complexity is even higher when the ITSs are applied on VEs because the new interaction possibilities offered by these environments must be considered as new key information pieces for student modelling, impacting all the educational process: the path followed by the student during their navigation through 3D scenarios; non-verbal behavior such as gaze direction; new types of hints or instructions that the tutoring module can provide to the student; new question types that the student can ask, etc. Thus, it is necessary for the ITS structure, which is embedded in the IVET, to be enriched by these aspects, while keeping a clear, structured and well defined architecture. Most approaches to SM on ITSs and IVETs don’t consider a complete enough taxonomy of possible knowledge about the student. In addition, most of them have validity only in certain domains or they are hard to be adapted for different ITSs. In order to overcome these limitations, we have proposed, in the framework of this doctoral research project, a newStudentModeling mechanism that is based onOntological Engineering and inspired on pedagogical principles, with a wide and flexible data model about the student that facilitates its adaptation and extension to different ITSs and learning applications, as well as a rich diagnosis method with non-monotonic reasoning capacities. The diagnosis method is able to infer the state of the learning objectives encompassed by the ITS and, fromit, the student’s knowledge state during the student’s process of learning. The proposed student modelling approach has been implemented and integrated in a software agent (the student modeling agent) within an existing software platform for the development of IVETs called MAEVIF. This platform was designed to be easily configurable for different learning applications. The proposed student modeling has been implemented and it has been instantiated for two types of learning environments: one for learning to use the graphical user interface of a software application and a Virtual Environment for procedural training. In addition, a methodology to guide on the application of this student modeling approach to each specific system has been developed

Scheduling Ontology Engineering Projects Using gOntt

In order to manage properly ontology development projects in complex settings and to apply correctly the NeOn Methodology, it is crucial to have knowledge of the entire ontology development life cycle before starting the development projects. The ontology project plan and scheduling helps the ontology development team to have this knowledge and to monitor the project execution. To facilitate the planning and scheduling of ontology development projects, the NeOn Toolkit plugin called gOntt has been developed. gOntt is a tool that supports the scheduling of ontology network development projects and helps to execute them. In addition, prescriptive methodological guidelines for scheduling ontology development projects using gOntt are provided