China
Africa
Explore chapters and articles related to this topic
Matching Lifestyle to Technology
Published in Ryuzo Furukawa, Lifestyle and Nature, 2019
Another way to expand and dig into a lifestyle is the method of action decomposition tree (Fig. 6.5). This introduces ontology engineering and creates a structure for lifestyles. Ontology engineering is very effective at providing a shared vocabulary, as well as a medium, or structure, for sharing and understanding knowledge. A function decomposition tree can be used to understand production facilities or functionality; it provides a systematic view of the functions of an artifact [2, 3]. With human behavior there are psychological elements that are separate from such functions of artifacts. However, lifestyles that include such intangible aspects can also be examined through ontology engineering, and an action decomposition tree can be made. This will give us a systematic structure for that lifestyle.
Ontologies for Knowledge Representation
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
Ontologies are getting more popular in multiple areas including semantic web search, natural language processing, artificial intelligence, bioinformatics, etc. Ontology engineering is related to technologies and tools used for building and managing ontologies. The primary goal of ontology construction methodology must be ensuring the clarity, extendibility, reusability, and reliability of the ontology. There are many challenges in the method of ontology construction because these methods are mostly applied to develop specific domain ontologies [44]. Many of these challenges are related to software engineering. If ontology development can be supported by the principles of software engineering, then the ontology can be made more reliable and adaptable [45].
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
Enriching the functionally graded materials (FGM) ontology for digital manufacturing
Published in International Journal of Production Research, 2021
Munira Mohd Ali, Ruoyu Yang, Binbin Zhang, Francesco Furini, Rahul Rai, J. Neil Otte, Barry Smith
The field of study concerned with applications of ontology within particular domains such as medicine or engineering is called applied ontology, and the activity involved in such use is termed ontology engineering (OE). Ontology engineers work to create and manage large-scale representations of entities such as actions, temporal intervals, physical objects, information artifacts, and so forth. Domain ontologies start from representations of categories of entities at very high levels of generality, which are then used by engineers as the starting point for creating representations of entities in specific domains through the specialisation of general terms. For example, the term ‘object’ is specialised to form the term ‘valve’, which is then dedicated further to ‘ball valve’, to ‘single-body ball valve’, and so on. In this way, a coordinated set of vocabularies is created that allows independent researchers to annotate data in a way that facilitates communication across multiple disciplinary groups (Smith 2004). In recent years, scientists working in OE have focused on applications in industrial engineering and related areas with the goal of creating re-usable and intuitive representations that allow researchers to manage, store, retrieve, and reuse their data effectively.
Human resource optimisation through semantically enriched data
Published in International Journal of Production Research, 2018
Damiano Arena, Apostolos Charalampos Tsolakis, Stylianos Zikos, Stelios Krinidis, Chrysovalantou Ziogou, Dimosthenis Ioannidis, Spyros Voutetakis, Dimitrios Tzovaras, Dimitris Kiritsis
Ontology model development has already become an engineering discipline, Ontology Engineering, which refers to ‘The set of activities that concern the ontology development process and the ontology life cycle, the methods and methodologies for ontology building, and the tool suites and languages that support them’ (Kiritsis 2011). The employment of ontology engineering technologies in the area of industrial data and information modelling has opened the path for exploiting ontologies towards providing formal definitions of the elements and their types, properties and interrelationships that exist for the domain of discourse (ISO/IEC 19763 2015; OWL Web Ontology Language Guide 2004; OWL Web Ontology Language Reference 2004). Ontologies can be modelled with different knowledge modelling techniques and they can be implemented in various kinds of languages based on different knowledge representation (KR) formalisms. Description logics (DLs), for example, are a well-known family of KR languages, which are widely used to model ontologies (Sikos 2017). The use of such logics can be traced back to the 1980s, but nowadays provide one of the main underpinnings for the web ontology language (OWL) as standardised by the – W3C – World Wide Web Consortium (Krötzsch, Simancik, and Horrocks 2012).
Ontology-Based decision tree model for prediction of fatty liver diseases
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Seyed Yashar Banihashem, Saman Shishehchi
An ontology is an explicit specification of a conceptualization (Gruber 2018), which combines machine learning and artificial intelligence. The most crucial feature of ontology is enabling knowledge sharing and reuse. Classes, attributes, individuals and properties are needed to define in the ontology. A class shows the description of a set of collections with the same characteristics. Classes offer the concept categorized on the ontology such as symptoms, patients, treatments. ‘Properties’ are descriptions of relationships between ontology classes or assign any data type value to instances. Attributes (relationship) describe the classes and make the relations among the concepts. Individuals (instances) constitute a series of concepts and relationships that have specific knowledge. Some ontology-based systems were developed for disease detection because of the constraints on the attributes used for consistency checking. Most of them developed an ontology for a given disease, detected the type of disease, and recommended the treatment using ontology (Shishehchi and Banihashem 2021). We developed ontology via protégé 5 (Musen & Protégé Team, 2015), the most famous ontology engineering tool. The ontology model proposed in this paper is designed according to all data in the decision tree created with Rapidminer. The results (rules) of the decision tree are transformed to SWRL rules which is acceptable for a knowledge-based system. To make decisions for patients depends on the question and answering of the medical staff involved in the healthcare system. For large volumes of data, ontology is one of the proposed solutions to deal with it (Chavan and Karyakarte 2020).