Exploring Naive Bayes Classifiers for Tabular Data to Knowledge Graph Matching - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Exploring Naive Bayes Classifiers for Tabular Data to Knowledge Graph Matching

Brice Foko
  • Fonction : Auteur
  • PersonId : 1378650
Azanzi Jiomekong
  • Fonction : Auteur
  • PersonId : 1146144
Hippolyte Tapamo
  • Fonction : Auteur
  • PersonId : 1378651
Sanju Tiwari
  • Fonction : Auteur
  • PersonId : 1378652

Résumé

The present research investigates the use of Naive Bayes classifiers to match knowledge graphs and tabular data, with particular emphasis on Column Type Annotation, Cell Entity Annotation, Column Property Annotation and Table Topic Detection. Using feature extraction techniques such as number of co-occurrences and term frequency, the study evaluates the effectiveness and performance of Naive Bayes classifiers on a variety of datasets. The proposed method is straightforward and generic, making a contribution to the field of knowledge graph matching and demonstrating the potential of Naive Bayes classifiers for the integration and interoperability of tabular data and knowledge graphs.
Fichier principal
Vignette du fichier
paper6.pdf (915.59 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04560328 , version 1 (26-04-2024)

Identifiants

  • HAL Id : hal-04560328 , version 1

Citer

Brice Foko, Azanzi Jiomekong, Hippolyte Tapamo, Jérémy Buisson, Sanju Tiwari. Exploring Naive Bayes Classifiers for Tabular Data to Knowledge Graph Matching. Semantic Web Challenge on Tabular Data to Knowledge Graph Matching 2023, Nov 2023, Athènes, Greece. pp.72-84. ⟨hal-04560328⟩

Collections

AFRIQ
0 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More