Human-centred, transparent and explainable AIs are 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 hybrid machine learning algorithms that combine symbolic and sub-symbolic learning. The project focuses on knowledge graphs with rich semantics as knowledge representation mechanism because of their increasing popularity across domains and industries in Europe.
Some explainable and transparent 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.
ENEXA devises new machine learning approaches that maintain formal guarantees pertaining to completeness and correctness while exploiting different representations (formal logics, embeddings and tensors) of knowledge graphs in a concurrent fashion. With our new methods, we plan to achieve significant advances in the scalability of machine learning, especially on knowledge graphs. A key innovation of ENEXA lies in its approach to explainability. Here, we focus on devising human-centred explainability techniques based on the concept of co-construction, where human and machine enter a conversation to co-construct human-understandable explanations. The resulting approach is deployed in three sectors of European significance, i.e., business services, geospatial intelligence and brand marketing.