Methodology for Multi-aspect Ontology Development

Smirnov, Alexander; Levashova, Tatiana; Shilov, Nikolay

doi:10.1007/978-3-030-73976-8_8

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…In [12], the authors present a framework for the building of multi-aspect ontologies that combines the aspect integration approach at various levels. Based on human-machine collective intelligence, the methodology is used to create a multiaspect ontology for decision support.…”

Section: A Ontology Development Methodologiesmentioning

confidence: 99%

“…Additionally, the transition from a data model to an ontology is not addressed, potentially increasing the manual workload involved in conceptualization. Stages 3 and 4 only involve manual ontology matching in the methodology proposed by [12]. The remaining steps closely align with the approach developed in our framework.…”

Section: Comparative Assessmentmentioning

confidence: 97%

“…Therefore, an ontology development framework that simplifies the ontology reusing process is required. We have compared the ontology development methodologies mentioned in [5]- [12]. These methods provide a better understanding of the process of ontology development and highlight the importance of ontology reuse and ontology merging.…”

Section: A Problem Statementmentioning

confidence: 99%

“…In order to demonstrate the strengths and advantages of our approach in comparison to existing methodologies, we provide a comprehensive analysis that compares our method with the state-of-the-art methodology for multi-aspect ontology development proposed in [12].…”

Section: Comparative Assessmentmentioning

confidence: 99%

See 3 more Smart Citations

Semi-Automatic Ontology Development Framework for Building Energy Data Management

Pan,

Gao,

Ponci

et al. 2023

IEEE Access

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With the ongoing digital transformation and multi-domain interaction occurring in the buildings, a huge amount of heterogeneous data is generated and stored on a daily basis. To take advantage of the gathered data and help better decision makings, suitable methods are needed to meet the demand for building operations and reinvestment planning. Ontology, which provides not only the vocabulary of a certain domain but also the relationship between each other has been used in multiple engineering fields to manage heterogeneous data. A plethora of ontology development methodologies have been developed in the last decade, whereas those methods are still really time-consuming and in a low degree of automation. In this paper, we approach the problem by first presenting a semi-automatic ontology development framework that integrates existing automatic ontology tools and reuses existing ontology and data model. Based on this framework, we create a building energy management ontology and evaluate the data coverage of several real-life data sets.

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Section: A Ontology Development Methodologiesmentioning

confidence: 99%

Section: Comparative Assessmentmentioning

confidence: 97%

Section: A Problem Statementmentioning

confidence: 99%

Section: Comparative Assessmentmentioning

confidence: 99%

See 2 more Smart Citations

Semi-Automatic Ontology Development Framework for Building Energy Data Management

Pan,

Gao,

Ponci

et al. 2023

IEEE Access

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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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Interoperability and Self-Organization in Human-Machine Collective Intelligence

Smirnov¹,

Ponomarev²

2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Methodology for Multi-aspect Ontology Development

Cited by 4 publications

References 16 publications

Semi-Automatic Ontology Development Framework for Building Energy Data Management

Semi-Automatic Ontology Development Framework for Building Energy Data Management

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

Interoperability and Self-Organization in Human-Machine Collective Intelligence

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