Identifying Relevant Data in RDF Sources

Zoé Chevallier (), Zoubida Kedad, Béatrice Finance and Frédéric Chaillan
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Zoé Chevallier: DAVID - Données et algorithmes pour une ville intelligente et durable - DAVID - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines
Zoubida Kedad: DAVID - Données et algorithmes pour une ville intelligente et durable - DAVID - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines
Béatrice Finance: DAVID - Données et algorithmes pour une ville intelligente et durable - DAVID - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines

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Abstract: The increasing number of RDF data sources published on the web represents an unprecedented amount of information. However, querying these sources to extract the relevant information for a specific need represented by a target schema is a complex task as the alignment between the target and the source schemas might not be provided or incomplete. This paper presents an approach which aims at automatically populating the classes of a target schema. Our approach relies on a semi-supervised learning algorithm that iteratively identifies instance patterns in the data source that represent candidate instances for the target schema. We present some preliminary experiments showing the effectiveness of our approach.

Keywords: RDF data sources; Semi-supervised learning; Target Schema Instantiation; Web Data extraction (search for similar items in EconPapers)
Date: 2024-05-14
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Published in 18th International Conference on Research Challenges in Information Science, RCIS 2024, May 2024, Guimarães, Portugal. pp.92-99, ⟨10.1007/978-3-031-59468-7_11⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04758283

DOI: 10.1007/978-3-031-59468-7_11

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