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Ontologies
Published in Weiming Shen, Douglas H. Norrie, Jean-Paul A. Barthès, Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing, 2019
Weiming Shen, Douglas H. Norrie, Jean-Paul A. Barthes
Recently Guarino and Giaretta (1995) have given a more compact definition: “Ontology is the branch of philosophy which deals with the nature and the organization of reality [...] Ontology tries to answer the question What is being? or, in a meaningful reformulation; What are the features common to all beings? The domain of Ontology has further been refined into Formal Ontology and Material Ontology, where Formal Ontology is concerned not so much with the bare existence of certain objects, but rather the rigorous description of their form of being, i.e., their structural features.” Finally Guarino and Giaretta indicate that “Ontology as such is usually contrasted with Epistemology [or theory of knowledge] which deals with the nature and the organization of reality.”
Standard Ontologies and HRI
Published in Paolo Barattini, Vicentini Federico, Gurvinder Singh Virk, Tamás Haidegger, Human–Robot Interaction, 2019
Sandro Rama Fiorini, Abdelghani Chibani, Tamás Haidegger, Joel Luis Carbonera, Craig Schlenoff, Jacek Malec, Edson Prestes, Paulo Gonçalves, S. Veera Ragavan, Howard Li, Hirenkumar Nakawala, Stephen Balakirsky, Sofiane Bouznad, Noauel Ayari, Yacine Amirat
The range of activities concerning the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies and the tools and languages that support them is called Ontology engineering. Nowadays, there are several different methodologies that can be adopted for developing an ontology engineering process, including METHONTOLOGY [FLGPJ97], KACTUS [SWJ95], On-To-Knowledge [SSS04], DILIGENT [DSV+05], NeOn [SFGPFL12] and so on. Most of these methodologies specify sequences (or cycles) of activities that should be carried out for developing an ontology, including Feasibility study, which is an assessment of the practicality of a proposed projectKnowledge acquisition, which is responsible for capturing the relevant domain knowledge from different sourcesConceptual modelling, whose goal is to structure the captured knowledge in a semi-formal ontology conceptual modelAxiomatization, which imposes a formal structure on the modelled domain knowledge (usually adopting a representation based on First Order Logics)Implementation, whose purpose is to implement the ontology in a computer-processable representation format, such as OWL*Evaluation, which evaluates the developed ontology for ensuring its qualityMaintenance, whose purpose is to fix errors, and keep the quality of the ontology when it is modified, by inclusion of novel knowledge or by updating some definitions
Using intelligent ontology technology to extract knowledge from successful project in IoT enterprise systems
Published in Enterprise Information Systems, 2022
Jinfeng Ding, TianRan Tang, Yaqin Zhang, Wi Chi
An ontology model conceptualises a domain into a machine-readable format. It uses the ontology to mean a specification of a condeptualization. That is, an ontology model is a description, like a formal specification of program pattern, of the concepts and relationships that can exist for a community of agents. The definition is consistent with the usage of ontology as a set of concept. The subject of ontology is the study of the categories of things that exist or may exist in some domain. An informal ontology may be specified by a catalogue of types that are either undefined or defined only by statements in a natural language. A formal ontology is specified by a collection of names for concept and relation types organised in a partial ordering by the type or subtype relation (Niles and Pease 2003; Suo 2003).
A non-conformance rate prediction method supported by machine learning and ontology in reducing underproduction cost and overproduction cost
Published in International Journal of Production Research, 2021
Bongjun Ji, Farhad Ameri, Hyunbo Cho
In this work, an ontological approach is used for representing the formal semantics of work orders. The proposed ontology is referred to as Work Order Ontology (WON) throughout this work. An ontology is an explicit formal specification of the terms in the domain and relations among them (Gruber 1993). The general purpose of ontologies is to provide a shared understanding of the information exchanged among various parties (Maedche et al. 2003). Ontologies are used for communication, computational reasoning, information and knowledge organisation, and exchange and reuse of knowledge (Usman 2012). Ontologies can be classified according to their level of generality under three categories, namely, Top-level ontology, Mid-level ontology, Domain ontology, and Application ontology (Rudnicki et al. 2016). Top-level ontologies, also known as foundational ontologies or upper ontologies, represent the general and abstract notions that can be used and interpreted across multiple domains. The use of top-level ontologies has many advantages. First, it enables effective alignment among the domain ontologies. Second, it promotes data interoperability and also reduces the ontology development time by providing reusable modelling patterns (Karray et al. 2019). Third, with top-level ontologies, the terms can be defined unambiguously and their intended meaning can be shared and understood effectively. Some of the widely used top-level ontologies are Basic Formal Ontology (BFO; Arp, Smith, and Spear 2015), Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE; Masolo et al. 2002), and Suggested Upper Merged Ontology (SUMO; Niles and Pease, 2001).