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

Authors Claudia d'Amato , Louis Mahon , Pierre Monnin , Giorgos Stamou

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Author Details

Claudia d'Amato
  • University of Bari, Italy
Louis Mahon
  • Oxford University, United Kingdom
Pierre Monnin
  • Université Côte d'Azur, Inria, CNRS, I3S, France
Giorgos Stamou
  • National Technical University of Athens, Greece

Funding

  • d'Amato, Claudia: Partially supported by the project FAIR - Future AI Research (PE00000013), spoke 6 - Symbiotic AI (https://future-ai-research.it/), under the NRRP MUR program funded by the NextGenerationEU and by the project HypeKG - Hybrid Prediction and Explanation with Knowledge Graphs (H53D23003700006), under PRIN 2022 program funded by MUR.
  • Monnin, Pierre: Supported by the AT2TA project (https://at2ta.loria.fr), funded by the French National Research Agency ("Agence Nationale de la Recherche" - ANR) under grant ANR-22-CE23-0023.

Cite AsGet BibTex

Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou. Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 8:1-8:35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/TGDK.1.1.8

Abstract

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.

Subject Classification

ACM Subject Classification
  • Information systems → World Wide Web
  • Computing methodologies → Artificial intelligence
Keywords
  • Graph-based Learning
  • Knowledge Graph Embeddings
  • Large Language Models
  • Explainable AI
  • Knowledge Graph Completion & Curation

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