A Review on Ontology Modularization Techniques - A Multi-Dimensional Perspective

Clair, Andrew Le; Marinache, Alicia; Ghalayini, Haya El; MacCaull, Wendy; Khédri, Ridha

doi:10.1109/tkde.2022.3152928

Cited by 3 publications

(2 citation statements)

References 85 publications

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“…It is composition of ODP. Le Clair and Khedri [24] developed algebraic technique based on logical technique to propose a modularization approach. The modules derived from set of Boolean sub-algebras which covered evaluation metrics for ontology modularization techniques: local correctness and local completeness.…”

Section: Related Workmentioning

confidence: 99%

“…The approaches here mentioned are not the only ones but the most cited in the literature. Nevertheless, others are cited in the study of d'Aquin et al [7,[25][26][27]. Among these approaches hereinbefore cited, only those described by Schlicht and Stuckenschmidt [8][9][10][11][12] concerned the top-down method which refers to the splitting up of ontology into modules.…”

Section: Related Workmentioning

confidence: 99%

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A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

Warda,

Ahmed,

Hayatou

et al. 2024

ISI

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Nowadays, ontologies are backbone of Semantic Web. Several domains use ontologies as knowledge models. As their number is constantly increasing, designers are opting to reuse some of those that exist to build new ones. When it is impossible to reuse a part depending on its organization, they import the whole ontology and this makes the manipulation cumbersome especially if the ontology has large concepts. Therefore, segmenting ontologies into partitions, if they are not yet, becomes a constant challenge for designers. This paper presents an approach to modularize ontology using hidden Markov model. Ontology triples are extracted within ontology through SPARQL queries and labelled with integers. The labelled triples constituted a Markov chain where ontology concepts are states and ontology relationships are symbols. This set is used to initialize HMM parameters such as states transition probabilities and symbols observation probabilities matrix and initial states probabilities vector. The transition probabilities matrix of HMM is then used as input of K-Means algorithm to generated modules of ontology concepts. This approach does not handled ontology axioms, which characterize heavy ontologies, and only lightweight ontologies are considered. Experiment on eighteen ontologies, obtained modules satisfied ontology modularization criteria such as independence, non-redundancy, correctness and completeness.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

Warda,

Ahmed,

Hayatou

et al. 2024

ISI

View full text Add to dashboard Cite

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DISEL: A Language for Specifying DIS-Based Ontologies

Wang

Chen

Alomair

et al. 2022

Knowledge Science, Engineering and Management

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Guidelines for the reuse of ontology content

Halper

Soldatova

Brochhausen

et al. 2023

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Reuse of elements from existing ontologies in the construction of new ontologies is a foundational principle in ontological design. It offers the benefits, among others, of consistency and interoperability between such knowledge structures as well as sharing resources. Reuse is widely found within important collections of established ontologies, such as BioPortal and the OBO Foundry. However, reuse comes with its own potential problems involving ontological commitment, granularity, and ambiguity. Guidelines are proposed to aid ontology developers and curators in their prospective reuse of content. These guidelines have been gleaned over years of practice in the ontology field. The guidelines are couched in experiential reports on designing and curating particular ontologies (e.g., EXACT and EXACT2) and using generally accepted approaches (e.g., MIREOT) in doing so. Various software tools to assist in ontology reuse are surveyed and discussed.

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A Review on Ontology Modularization Techniques - A Multi-Dimensional Perspective

Cited by 3 publications

References 85 publications

A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

DISEL: A Language for Specifying DIS-Based Ontologies

Guidelines for the reuse of ontology content

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