Secured Ontology Matching Using Graph Matching

Shenoy, K. Manjula; Shet, K C; Acharya, Dinesh U

doi:10.1007/978-3-642-31552-7_2

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Machine learning-based methods for ontology alignment [16] and ontology matching [7] can be applied to automatically map concepts and terms from one ontology or vocabulary to another. These algorithms use techniques such as semantic similarity measures [25], graph-based methods [24], and deep learning models [4,10,11] to identify correspondences between concepts in different ontologies or vocabularies. The goal is to produce a mapping that enables data exchange between systems using different ontologies or vocabularies while preserving the meaning of the data.…”

Section: Ontology and Vocabulary Alignmentmentioning

confidence: 99%

Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective

Boukhers

Lange

Beyan

2023

Companion Proceedings of the ACM Web Conference 2023

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Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community. CCS CONCEPTS• Information systems → Data exchange; Data access methods; Semantic web description languages.

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