Identifying Relevant Data in RDF Sources
Zoé Chevallier (zoechevallier@yahoo.com),
Zoubida Kedad,
Béatrice Finance and
Frédéric Chaillan
Additional contact information
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
Post-Print from HAL
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
References: Add references at CitEc
Citations:
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⟩
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04758283
DOI: 10.1007/978-3-031-59468-7_11
Access Statistics for this paper
More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD (hal@ccsd.cnrs.fr).