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Communication Dans Un Congrès Année : 2020

DAGOBAH: Enhanced Scoring Algorithms for Scalable Annotations of Tabular Data

Viet-Phi Huynh
  • Fonction : Auteur
Yoan Chabot
  • Fonction : Auteur
  • PersonId : 1268049
Thomas Labbé
  • Fonction : Auteur
Pierre Monnin
Raphaël Troncy

Résumé

We present new approaches used in the DAGOBAH system to perform automatic semantic table interpretation. DAGOBAH semantically annotates tables with Wikidata entities and relations to perform three tasks: Columns-Property Annotation (CPA), Cell-Entity Annotation (CEA) and Column-Type Annotation (CTA). In our system, the initial scores from entity disambiguation influence the CPA output, which, in turn, influences the output of the CEA. Finally, the CTA is computed using the type hierarchy available in the knowledge graph in order to annotate columns with the most suitable fine-grained types. This approach that leverages mutual influences between annotations allows DAGOBAH to obtain very competitive results on all tasks of the SemTab2020 challenge.
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Dates et versions

hal-04170851 , version 1 (25-07-2023)

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  • HAL Id : hal-04170851 , version 1

Citer

Viet-Phi Huynh, Jixiong Liu, Yoan Chabot, Thomas Labbé, Pierre Monnin, et al.. DAGOBAH: Enhanced Scoring Algorithms for Scalable Annotations of Tabular Data. Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020) co-located with the 19th International Semantic Web Conference (ISWC 2020), Nov 2020, Athènes (en ligne), Greece. pp.3. ⟨hal-04170851⟩

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