An Ontology for Defect Detection in Metal Additive Manufacturing

A key challenge for Industry 4.0 applications is to develop control systems for automated manufacturing services that are capable of addressing both data integration and semantic interoperability issues, as well as monitoring and decision making tasks. To address such an issue in advanced manufacturing systems, principled knowledge representation approaches based on formal ontologies have been proposed as a foundation to information management and maintenance in presence of heterogeneous data sources. In addition, ontologies provide reasoning and querying capabilities to aid domain experts and end users in the context of constraint validation and decision making. Finally, ontology-based approaches to advanced manufacturing services can support the explainability and interpretability of the behaviour of monitoring, control, and simulation systems that are based on black-box machine learning algorithms. In this work, we provide a novel ontology for the classification of process-induced defects known from the metal additive manufacturing literature. Together with a formal representation of the characterising features and sources of defects, we integrate our knowledge base with state-of-the-art ontologies in the field. Our knowledge base aims at enhancing the modelling capabilities of additive manufacturing ontologies by adding further defect analysis terminology and diagnostic inference features

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

A formal model for fuzzy ontologies.

Au Yeung Ching Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 97-110).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Semantic Web and Ontologies --- p.3Chapter 1.2 --- Motivations --- p.5Chapter 1.2.1 --- Fuzziness of Concepts --- p.6Chapter 1.2.2 --- Typicality of Objects --- p.6Chapter 1.2.3 --- Context and Its Effect on Reasoning --- p.8Chapter 1.3 --- Objectives --- p.9Chapter 1.4 --- Contributions --- p.10Chapter 1.5 --- Structure of the Thesis --- p.11Chapter 2 --- Background Study --- p.13Chapter 2.1 --- The Semantic Web --- p.14Chapter 2.2 --- Ontologies --- p.16Chapter 2.3 --- Description Logics --- p.20Chapter 2.4 --- Fuzzy Set Theory --- p.23Chapter 2.5 --- Concepts and Categorization in Cognitive Psychology --- p.25Chapter 2.5.1 --- Theory of Concepts --- p.26Chapter 2.5.2 --- Goodness of Example versus Degree of Typicality --- p.28Chapter 2.5.3 --- Similarity between Concepts --- p.29Chapter 2.5.4 --- Context and Context Effects --- p.31Chapter 2.6 --- Handling of Uncertainty in Ontologies and Description Logics --- p.33Chapter 2.7 --- Typicality in Models for Knowledge Representation --- p.35Chapter 2.8 --- Semantic Similarity in Ontologies and the Semantic Web --- p.39Chapter 2.9 --- Contextual Reasoning --- p.41Chapter 3 --- A Formal Model of Ontology --- p.44Chapter 3.1 --- Rationale --- p.45Chapter 3.2 --- Concepts --- p.47Chapter 3.3 --- Characteristic Vector and Property Vector --- p.47Chapter 3.4 --- Subsumption of Concepts --- p.49Chapter 3.5 --- Likeliness of an Individual in a Concept --- p.51Chapter 3.6 --- Prototype Vector and Typicality --- p.54Chapter 3.7 --- An Example --- p.59Chapter 3.8 --- Similarity between Concepts --- p.61Chapter 3.9 --- Context and Contextualization of Ontology --- p.65Chapter 3.9.1 --- Formal Definitions --- p.67Chapter 3.9.2 --- Contextualization of an Ontology --- p.69Chapter 3.9.3 --- "Contextualized Subsumption Relations, Likeliness, Typicality and Similarity" --- p.71Chapter 4 --- Discussions and Analysis --- p.73Chapter 4.1 --- Properties of the Formal Model for Fuzzy Ontologies --- p.73Chapter 4.2 --- Likeliness and Typicality --- p.78Chapter 4.3 --- Comparison between the Proposed Model and Related Works --- p.81Chapter 4.3.1 --- Comparison with Traditional Ontology Models --- p.81Chapter 4.3.2 --- Comparison with Fuzzy Ontologies and DLs --- p.82Chapter 4.3.3 --- Comparison with Ontologies modeling Typicality of Objects --- p.83Chapter 4.3.4 --- Comparison with Ontologies modeling Context --- p.84Chapter 4.3.5 --- Limitations of the Proposed Model --- p.85Chapter 4.4 --- "Significance of Modeling Likeliness, Typicality and Context in Ontologies" --- p.86Chapter 4.5 --- Potential Application of the Model --- p.88Chapter 4.5.1 --- Searching in the Semantic Web --- p.88Chapter 4.5.2 --- Benefits of the Formal Model of Ontology --- p.90Chapter 5 --- Conclusions and Future Work --- p.91Chapter 5.1 --- Conclusions --- p.91Chapter 5.2 --- Future Research Directions --- p.93Publications --- p.96Bibliography --- p.9

Decision support system for in-flight emergency events

Medical problems during flight have become an important issue as the number of passengers and miles flown continues to increase. The case of an incident in the plane falls within the scope of the healthcare management in the context of scarce resources associated with isolation of medical actors working in very complex conditions, both in terms of human and material resources. Telemedicine uses information and communication technologies to provide remote and flexible medical services, especially for geographically isolated people. Therefore, telemedicine can generate interesting solutions to the medical problems during flight. Our aim is to build a knowledge-based system able to help health professionals or staff members addressing an urgent situation by given them relevant information, some knowledge, and some judicious advice. In this context, knowledge representation and reasoning can be correctly realized using an ontology that is a representation of concepts, their attributes, and the relationships between them in a particular domain. Particularly, a medical ontology is a formal representation of a vocabulary related to a specific health domain. We propose a new approach to explain the arrangement of different ontological models (task ontology, inference ontology, and domain ontology), which are useful for monitoring remote medical activities and generating required information. These layers of ontologies facilitate the semantic modeling and structuring of health information. The incorporation of existing ontologies [for instance, Systematic Nomenclature Medical Clinical Terms (SNOMED CT)] guarantees improved health concept coverage with experienced knowledge. The proposal comprises conceptual means to generate substantial reasoning and relevant knowledge supporting telemedicine activities during the management of a medical incident and its characterization in the context of air travel. The considered modeling framework is sufficiently generic to cover complex medical situations for isolated and vulnerable populations needing some care and support services

Semantic model-driven development of web service architectures.

Building service-based architectures has become a major area of interest since the advent of Web services. Modelling these architectures is a central activity. Model-driven development is a recent approach to developing software systems based on the idea of making models the central artefacts for design representation, analysis, and code generation. We propose an ontology-based engineering methodology for semantic model-driven composition and transformation of Web service architectures. Ontology technology as a logic-based knowledge representation and reasoning framework can provide answers to the needs of sharable and reusable semantic models and descriptions needed for service engineering. Based on modelling, composition and code generation techniques for service architectures, our approach provides a methodological framework for ontology-based semantic service architecture

Cognitive context and arguments from ontologies for learning

The deployment of learning resources on the web by different experts has resulted in the accessibility of multiple viewpoints about the same topics. In this work we assume that learning resources are underpinned by ontologies. Different formalizations of domains may result from different contexts, different use of terminology, incomplete knowledge or conflicting knowledge. We define the notion of cognitive learning context which describes the cognitive context of an agent who refers to multiple and possibly inconsistent ontologies to determine the truth of a proposition. In particular we describe the cognitive states of ambiguity and inconsistency resulting from incomplete and conflicting ontologies respectively. Conflicts between ontologies can be identified through the derivation of conflicting arguments about a particular point of view. Arguments can be used to detect inconsistencies between ontologies. They can also be used in a dialogue between a human learner and a software tutor in order to enable the learner to justify her views and detect inconsistencies between her beliefs and the tutor’s own. Two types of arguments are discussed, namely: arguments inferred directly from taxonomic relations between concepts, and arguments about the necessary an

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources