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Efficient Explainable Learning on Knowledge Graphs

CORDIS · observation · Publication date unknown

Efficient Explainable Learning on Knowledge Graphs Explainable Artificial Intelligence (AI) is key to achieving a human-centred and ethical development of digital and industrial solutions. ENEXA builds upon novel and promising results in knowledge representation and machine learning to develop scalable, transparent and explainable machine learning algorithms for knowledge graphs. The project focuses on knowledge graphs because of their critical role as enabler of new solutions across domains and industries in Europe. Some of the existing machine learning approaches for knowledge graphs are known to already provide guarantees with respect to their completeness and correctness. However, they are still impossible or impractical to deploy on real-world data due to the scale, incompleteness and inconsistency of knowledge graphs in the wild. We devise approaches that maintain formal guarantees pertaining to completeness and correctness while being able to

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recordType
award
status
SIGNED
region
EU
value
3991269.5
unit
EUR

Evidence & attribution

European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.

License: CORDIS reuse policy

First collected: 2026-09-20T00:21:03.701Z. This is not the publication date.