Application of concepts of neighbours to knowledge graph completion - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue CODATA Data Science Journal Année : 2021

Application of concepts of neighbours to knowledge graph completion

Résumé

The open nature of Knowledge Graphs (KG) often implies that they are incomplete. Knowledge graph completion (a.k.a. link prediction) consists in inferring new relationships between the entities of a KG based on existing relationships. Most existing approaches rely on the learning of latent feature vectors for the encoding of entities and relations. In general however, latent features cannot be easily interpreted. Rule-based approaches offer interpretability but a distinct ruleset must be learned for each relation. In both latent- and rule-based approaches, the training phase has to be run again when the KG is updated. We propose a new approach that does not need a training phase, and that can provide interpretable explanations for each inference. It relies on the computation of Concepts of Nearest Neighbours (C-NN) to identify clusters of similar entities based on common graph patterns. Different rules are then derived from those graph patterns, and combined to predict new relationships. We evaluate our approach on standard benchmarks for link prediction, where it gets competitive performance compared to existing approaches.
Fichier principal
Vignette du fichier
paper3.pdf (424.46 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03531781 , version 1 (18-01-2022)

Identifiants

Citer

Sébastien Ferré. Application of concepts of neighbours to knowledge graph completion. CODATA Data Science Journal, 2021, 4 (1), pp.1-28. ⟨10.3233/DS-200030⟩. ⟨hal-03531781⟩
88 Consultations
107 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More