Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

Hnatkowska, Bogumiła; Kozierkiewicz, Adrianna; Pietranik, Marcin

doi:10.1109/access.2020.3000035

Cited by 11 publications

(2 citation statements)

References 21 publications

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“…Although their method is effective, it does not cover other relevant ontology elements, such as logical axioms and annotations. Many studies [15,[19][20][21][22][23] utilized the lexical database WordNet in their ontology-merging methods due to their effectiveness in the semantic analysis of terminologies. Our proposed algorithm also utilized WordNet for synonym extraction purposes.…”

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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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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“…Several semi-automatic methods of ontology integration have been proposed, such as semi-automatic generation of property semantics based on ontology integration [17] and automatic generation of new ontology patterns based on ontology reuse. They include an ontology integration method based on knowledge graphs and machine learning technology, ontology integration [18] based on ontology matching and its related technologies [19], the model ranking of evaluating ontology based on semantic matching [20] and the method of constructing ontology from an ontology pattern [21] and compatibility index for comparing and aggregating ontologies [22].…”

Section: Related Workmentioning

confidence: 99%

Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas

et al. 2021

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Knowledge graph-based data integration is a practical methodology for heterogeneous legacy database-integrated service construction. However, it is neither efficient nor economical to build a new cross-domain knowledge graph on top of the schemas of each legacy database for the specific integration application rather than reusing the existing high-quality knowledge graphs. Consequently, a question arises as to whether the existing knowledge graph is compatible with cross-domain queries and with heterogenous schemas of the legacy systems. An effective criterion is urgently needed in order to evaluate such compatibility as it limits the quality upbound of the integration. This research studies the semantic similarity of the schemas from the aspect of properties. It provides a set of in-depth criteria, namely coverage and flexibility, to evaluate the pairwise compatibility between the schemas. It takes advantage of the properties of knowledge graphs to evaluate the overlaps between schemas and defines the weights of entity types in order to perform precise compatibility computation. The effectiveness of the criteria obtained to evaluate the compatibility between knowledge graphs and cross-domain queries is demonstrated using a case study.

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