An approach to merge domain ontologies using granular computing

Priya, M. Geetha; Cherukuri, Aswani Kumar

doi:10.1007/s41066-019-00193-3

Cited by 9 publications

(8 citation statements)

References 23 publications

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“… allows integrating knowledge represented by different formalisms unlike the multilingual [32]- [34] or granular ontologies [40], [41] at that preserving formalisms that are beneficial for knowledge representation in aspects;…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Granular ontologies offer another way to integrate multiple aspects [40], [41]. They propose granular perspectives for a specific ontological commitment.…”

Section: B Integration Of Multiple Aspectsmentioning

confidence: 99%

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

2021

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“…In [16], the authors proposed a novel method for merging domain ontologies utilizing granular computing. The method consists of four major operations, including association, isolation, purification, and reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Rudwan,

Fonou-Dombeu

2024

BDCC

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Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.

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“…The evaluation of the algorithms was done with precision. Priya and Kumar [46] presented a granular computing approach for mapping numerous existing ontologies into a single representative domain ontology. It is made up of four granular computing processes: association, isolation, purification, and reduction, which can be used to unify a set of related nodes in ontologies.…”

Section: Ontology Equality Of Conceptsmentioning

confidence: 99%

“…Two fundamental activities take place at this hub: This step may seem quite trivial, especially if done manually, but automating it would require a major formalisation of EQUALITY of two concepts to be able to say that a given concept already exists in the knowledge base. In general, the notion of ontology equality of concepts is still not well-defined and there is no clear consensus on this [42][43][44][45][46][47][48][49][50][51], especially when the new knowledge is from a sentence, which would obviously contain much less content compared to a concept already defined in an existing knowledge base. Therefore, this paper, in its specificity, proposes a technique known as Facts Enrichment Approach (FEA) for the identification and insertion/hooking of a new concept (C) from a sentence into an existing ontology (BO) by considering the notions of equality, similarity, and equivalence of concepts to develop a Target Ontology (TO).…”

Section: Acquisition Hubmentioning

confidence: 99%

Ontology-Based Methodology for Knowledge Acquisition from Groupware

et al. 2022

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Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware.

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An approach to merge domain ontologies using granular computing

Cited by 9 publications

References 23 publications

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

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

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Ontology-Based Methodology for Knowledge Acquisition from Groupware

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