Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Transactions on Graph Data and Knowledge Année : 2023

Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges

Résumé

The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGE) and deep neural networks (DNNs). The latter methods have been proved lately very efficient, leading the current AI spring. In this vision paper, we introduce some of the main existing methods for combining KGs and ML, divided into two categories: those using ML to improve KGs, and those using KGs to improve results on ML tasks. From this introduction, we highlight research gaps and perspectives that we deem promising and currently under-explored for the involved research communities, spanning from KG support for LLM prompting, integration of KG semantics in ML models to symbol-based methods, interpretability of ML models, and the need for improved benchmark datasets. In our opinion, such perspectives are stepping stones in an ultimate view of KGs as central assets for neuro-symbolic and explainable AI.
Fichier principal
Vignette du fichier
TGDK.1.1.8.pdf (1.02 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Licence : CC BY - Paternité

Dates et versions

hal-04353543 , version 1 (19-12-2023)

Licence

Paternité

Identifiants

Citer

Claudia d'Amato, Louis Mahon, Pierre Monnin, Giorgos Stamou. Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges. Transactions on Graph Data and Knowledge, 2023, 1 (1), pp.1-35. ⟨10.4230/TGDK.1.1.8⟩. ⟨hal-04353543⟩
257 Consultations
287 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More