Schema Matching for Semi-structured and Linked Data

Kettouch, Mohamed S. and Luca, Cristina L. and Hobbs, Mike (2017) Schema Matching for Semi-structured and Linked Data. In: 2017 IEEE 11th International Conference on Semantic Computing (ICSC), San Diego, CA, USA.

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The Linked Data paradigm is a common standard initiated to complement the general architecture of the semantic web and create a single space containing data that is machine-readable and connected to related data. Figures, however, show that one of the main sources of semi-structured data providers, Web APIs, continued to grow even after the creation of the Linked Data concept. Given that data sources with significant value are still in a semi-structured format, it is essential to bridge between the two data models, so that the full potential of the semantic web can be realised. This paper presents SimiMatch, an approach for schema matching between semi-structured and Linked Data. It contributes towards a virtual integration system that will be able to provide transparent access to heterogeneous and autonomous sources. It addresses the challenge of sustaining the continuous changes of the web of data via semantic similarity measurement.

Item Type: Conference or Workshop Item (Paper)
Keywords: Semantics, Conferences, Data mining, Vocabulary, Data integration, Cloud computing
Faculty: ARCHIVED Faculty of Science & Technology (until September 2018)
Depositing User: Lisa Blanshard
Date Deposited: 10 Sep 2019 14:28
Last Modified: 09 Sep 2021 16:12

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