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

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

gOntt, a Tool for Scheduling and Executing Ontology Development Projects

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