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Schema Matching for Semi-structured and Linked Data
conference contribution
posted on 2023-07-26, 14:44 authored by Mohamed S. Kettouch, Cristina L. Luca, Mike HobbsThe 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.
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Page range
270-271External DOI
Title of book
2017 IEEE 11th International Conference on Semantic Computing (ICSC)ISBN
978-1-5090-4284-5Conference proceeding
2017 IEEE 11th International Conference on Semantic Computing (ICSC)Name of event
2017 IEEE 11th International Conference on Semantic Computing (ICSC)Location
San Diego, CA, USAEvent start date
2017-01-31Event finish date
2017-02-01Language
- other
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2019-09-10Legacy Faculty/School/Department
ARCHIVED Faculty of Science & Technology (until September 2018)Usage metrics
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