Ontology: Components and Evaluation, a Review

Reyes-Peña, Cecilia; Vidal, Mireya Tovar

doi:10.13053/rcs-148-3-21

Cited by 17 publications

(10 citation statements)

References 30 publications

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“…Improving the quality of teaching schoolchildren can be achieved through the semantic description of knowledge about the subject area using methods of ontological engineering. The method of ontological engineering implies building a three-tier model of the field under study: top-tier ontologies, domain-specific ontology, and applied ontologies [29], [30]. The sequence of actions for developing an ontology.…”

Section: Methodsmentioning

confidence: 99%

Application of ontology-based engineering and stem approach in learning

Madina,

Saltanat,

Gulnaz

et al. 2023

IJEECS

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Currently, there is a need to improve the educational system and develop interdisciplinary studies at all stages of education, from school to postgraduate education. The implementation of cross-curriculum connections promotes a holistic view of natural phenomena and the relationship between them, that is, this knowledge becomes more meaningful and applicable in practice. The article proposes an approach to building a conceptual model of the content of education in the form of a thesaurus and ontology, the use of which will ensure that the educational information is adaptively selected and put straight. The article discusses the possibility of and experience in using ontological modeling and engineering for the conceptual description of school and higher education. The article discusses the development of the ontological science, technology, engineering, and mathematics (STEM) Education at School model. The article builds an ontological model, which is an integration of the ontology of school and the ontology of university. When filling in the knowledge base, it becomes possible to identify interdisciplinary relationships. The use of ontological engineering methods will improve the quality of education for schoolchildren and students through the semantic description of knowledge of the subject area by using interdisciplinary and STEM approaches in the process of education.

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Section: Methodsmentioning

confidence: 99%

Application of ontology-based engineering and stem approach in learning

Madina,

Saltanat,

Gulnaz

et al. 2023

IJEECS

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show abstract

“…Generally, the purpose of ontology evaluation is being conducted to ensure the quality and the accuracy of ontology elements (concepts, relations, and keywords) [32]. Besides that, it is important to evaluate the ontology's main aspects to guarantee that representation is the most real according to the domain [5]. Therefore, in this paper, we have identified the evaluation methods used in these papers.…”

Section: Answer To Research Question Rq4 What Evaluation Methods Was ...mentioning

confidence: 99%

“…Ontologies reflecting domain knowledge were utilized to drive the application's design and provide the system with semantic technology capabilities [4]. Before forming the ontology, the ontology engineer must first identify the main elements, which include concepts, relationships, functions, individuals or instances, and axioms [5]. These five components as a formalization of knowledge in ontologies [6].…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review of automatic ontology construction

Zulkipli

Maskat

Teo

2022

IJEECS

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In recent years, ontologies have received a lot of attention as a knowledge representation technique. Constructing an ontology can be a difficult task for several reasons: it necessitates time-consuming expert work, the classification task is not as simple as it appears, and the incredible speed with which knowledge evolves in the real world forces ontology engineers to constantly update and enrich the generated ontologies with new concepts, terms, and lexicon. However, there are other ways to automate ontology construction, such as semi-automatically, where human intervention is required in one or more ontology design tasks, and fully automatically, where the entire ontology construction is delegated to a software system. In this paper, we will conduct a systematic review of literature that will focus on a comparative analysis of different techniques relating to both semi-automatic and fully automatic ontology construction using various techniques and automated algorithms applied. In these fields, the goal is to identify the domain areas, current trends, data architectures, and ongoing challenges. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. The fields that have been investigated in this survey include online retail, biomedical, public security, information security (IS), Quran, Arabic, Dubai government services, Alzheimer’s disease, agriculture, Chinese tax, job portal, sentiment, and ontology learning. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.

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“…Attributes are the properties and characteristics that assert an ontology's data to discriminate and reason in the environment. Instances, also known as individuals or objects, are the lowest-level components of an ontology and is ultimately the element ontologies set out to describe or classify [37]. Axioms are given in the Semantic Web Rule (SWR) Language (SWRL), presented as constraints to limit possible "interpretations for the defined terms (p.3)" [36].…”

Section: Methodology a Ontology Design And Evaluationmentioning

confidence: 99%

Onto4MAT: A Swarm Shepherding Ontology for Generalized Multiagent Teaming

2022

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Research in multi-agent teaming has increased substantially over recent years. Underneath these attempts sits a suite of communication functions to enable effective teaming. The Artificial Intelligence (AI) systems supporting the teaming arrangement have mostly relied on knowledge-based systems, with a set of rules triggered based on mode of interaction. Enabling humans to effectively join the team of AI agents calls for both the humans and AI agents to share their understanding and representation of their shared worlds. Such shared understanding requires formal representations of concepts to support transparency during bi-directional communications between team members. Little research has been done in this space, especially when humans need to team with a swarm of agents. To address this research gap, we present an ontology designed specifically for human-agent teaming. The ontology is general, but we then contextualise it into a particular swarm-shepherding scenario to illustrate how it can be used in a particular context. The proposed Ontology for Generalised Multi-Agent Teaming Onto4MAT offers the underlying building blocks for effective communication and shared understanding between humans and multi-agent teams.

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Ontology: Components and Evaluation, a Review

Cited by 17 publications

References 30 publications

Application of ontology-based engineering and stem approach in learning

Application of ontology-based engineering and stem approach in learning

A systematic literature review of automatic ontology construction

Onto4MAT: A Swarm Shepherding Ontology for Generalized Multiagent Teaming

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