HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Neural Knowledge Base Repairs

Abstract : The curation of a knowledge base is a crucial but costly task. In this work, we suggest to make use of the advances in neural network research to improve the automated correction of constraint violations. Our method is a deep learning refinement of "Learning how to correct a knowledge base from the edit history", and similarly uses the edits that solved some violations in the past to infer how to solve similar violations in the present. Our system makes use of the graph content, literal embeddings, and features extracted from Web pages to improve its performance. The experimental evaluation on Wikidata shows significant improvements over baselines.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03325101
Contributor : Thomas Pellissier Tanon Connect in order to contact the contributor
Submitted on : Tuesday, August 24, 2021 - 1:18:27 PM
Last modification on : Tuesday, October 19, 2021 - 11:14:16 AM
Long-term archiving on: : Friday, November 26, 2021 - 9:26:26 AM

File

bass(2).pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Thomas Pellissier Tanon, Fabian Suchanek. Neural Knowledge Base Repairs. European Semantic Web Conference, Jun 2021, Hersonissos (virtual), Greece. pp.287-303, ⟨10.1007/978-3-030-77385-4_17⟩. ⟨hal-03325101⟩

Share

Metrics

Record views

32

Files downloads

20