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Identifying Relevant Data in RDF Sources

Author

Listed:
  • Zoé Chevallier

    (DAVID - Données et algorithmes pour une ville intelligente et durable - DAVID - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines, 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, 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, UVSQ - Université de Versailles Saint-Quentin-en-Yvelines)

  • Frédéric Chaillan

Abstract

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.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.

Suggested Citation

  • Zoé Chevallier & Zoubida Kedad & Béatrice Finance & Frédéric Chaillan, 2024. "Identifying Relevant Data in RDF Sources," Post-Print hal-04758283, HAL.
    • Handle: RePEc:hal:journl:hal-04758283
    DOI: 10.1007/978-3-031-59468-7_11
    as

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