An Improved Methodology for Collaborative Construction of Reusable, Localized, and Shareable Ontology

Sattar, Abdul; Ahmad, Mohammad Nazir; Surin, Ely Salwana Mat; Mahmood, Ahmad Kamil

doi:10.1109/access.2021.3054412

Cited by 13 publications

(16 citation statements)

References 59 publications

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“…Various ontology design methodologies are analysed and waste management ontology is constructed with the parameter of reuse, interoperability, and knowledge acquisition, but how the interoperability is achieved is not proved [8]. The learning ontology is constructed using pre-processed electronic textbooks and NLP techniques [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

Shanmurthy

Poongodi

Balusamy

et al. 2022

IJEECS

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Semantic web technology is adapted to the internet of things (IoT) for web - based applications to globally connect the services. Web ontology language (OWL) domain ontology is a powerful machine - readable language for domain knowledge representation. The developer stored the IoT application relevant ontology in a repository or catalogue. Hence, IoT application - related ontology files are available for reus e, but many of the IoT application - relevant ontology files are publicly not available or inaccessible. The proposed idea is to extract the contextual knowledge of IoT applications that contain inaccessible ontology files. The context - wise specific domain I oT applications are not obtainable, hence respective ontology - based research papers are identified and their frequent terms are computed. The selected contextual dominant frequent terms from the transport domain are passed into the skip - gram flavour of wor d2vector modelled n atural language processing ( NLP ) corpus which produces most similar terms. The domain experts select the appropriate terms to annotate in OWL ontology for contextual knowledge augmentation. Finally, 1422 contextual terms were generated b ased on dominant terms of selected IoT applications.

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Section: Literature Reviewmentioning

confidence: 99%

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

Shanmurthy

Poongodi

Balusamy

et al. 2022

IJEECS

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“…The merge or integration of two or more schemes can also solve domain knowledge representation problems in structured knowledge management. The authors of [17,31] argued that the best scheme (compared with single or integrated approaches) is one that satisfies the user requirements for a knowledge management system.…”

Section: E Neural Network (Connectionist)mentioning

confidence: 99%

“…However, most the considered studies did not provide detailed descriptions of their techniques, which could lead to problems in the design of other domain ontologies. However, the authors of some schemes did provide sufficient specific details [13][14][15][16]31].…”

Section: Concept Clusteringmentioning

confidence: 99%

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Ecological and Confined Domain Ontology Construction Scheme Using Concept Clustering for Knowledge Management

et al. 2022

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Knowledge management in a structured system is a complicated task that requires common, standardized methods that are acceptable to all actors in a system. Ontology, in this regard, is a primary element and plays a central role in knowledge management, interoperability between various departments, and better decision making. The ontology construction for structured systems comprises logical and structural complications. Researchers have already proposed a variety of domain ontology construction schemes. However, these schemes do not involve some important phases of ontology construction that make ontologies more collaborative. Furthermore, these schemes do not provide details of the activities and methods involved in the construction of an ontology, which may cause difficulty in implementing the ontology. The major objectives of this research were to provide a comparison between some existing ontology construction schemes and to propose an enhanced ecological and confined domain ontology construction (EC-DOC) scheme for structured knowledge management. The proposed scheme introduces five important phases to construct an ontology, with a major focus on the conceptualizing and clustering of domain concepts. In the conceptualization phase, a glossary of domain-related concepts and their properties is maintained, and a Fuzzy C-Mean soft clustering mechanism is used to form the clusters of these concepts. In addition, the localization of concepts is instantly performed after the conceptualization phase, and a translation file of localized concepts is created. The EC-DOC scheme can provide accurate concepts regarding the terms for a specific domain, and these concepts can be made available in a preferred local language.

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“…The improved methodology for collaborative construction of reusable, localized, and shareable ontologies proposed in the domain of Interlocking Institutional Worlds aims to adapt the best practices from the ontology development methodologies [25]. The methodology assumes ontology integration to include ontologies of related domains or subdomains into the developed ontology.…”

Section: Work On Problemmentioning

confidence: 99%

Methodology for Multi-Aspect Ontology Development: Ontology for Decision Support Based on Human-Machine Collective Intelligence

2021

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The advantage of human-machine collective intelligence for decision support systems is the ability to make better decisions due to the mitigation of human biases in the generation of potential solutions and their evaluation. So far, the potential of human-machine collective intelligence was used only in few decision support systems, however teamwork between humans and machines has not been achieved. This is partly due to the lack of interoperability in these systems. In earlier works, the authors proposed the apparatus of multi-aspect ontologies implying the integration of multiple domain ontologies to provide interoperability between humans and machines and coordinate interrelated processes going on in the systems of the considered type. Such ontologies have proved efficient for systems that require intensive information and knowledge exchange between loosely-related dynamic autonomous domains (e.g., enterprise knowledge management, product lifecycle management, or human-machine collective intelligence systems). However, existing ontology development methodologies fail to recommend a process that would support cross-domain knowledge integration during the multi-aspect ontology development. Moreover, the structure of the multiaspect ontology imposes some restrictions on the integration approach. The paper proposes such a methodology for the multi-aspect ontology development that incorporates the aspect integration approach at multiple levels. The methodology is applied to develop a multi-aspect ontology for decision support based on human-machine collective intelligence. An example from the "e-tourism" domain demonstrates the applicability of the proposed methodology as well as the usage of the multi-aspect ontology for a humanmachine environment aimed at solving real-world problems. The proposed methodology can facilitate the development of ontologies for complex knowledge-based systems that operate with knowledge from multiple loosely-connected domains.

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An Improved Methodology for Collaborative Construction of Reusable, Localized, and Shareable Ontology

Cited by 13 publications

References 59 publications

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

Ecological and Confined Domain Ontology Construction Scheme Using Concept Clustering for Knowledge Management

Methodology for Multi-Aspect Ontology Development: Ontology for Decision Support Based on Human-Machine Collective Intelligence

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