ENEXA: Efficient Explainable Learning on Knowledge Graphs

The ENEXA project builds upon 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. 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 has devised new machine learning approaches that maintain formal guarantees pertaining to completeness and correctness while exploiting diff erent representations (formal logics, embeddings and tensors) of knowledge graphs in a concurrent fashion. With these new methods, the project has advanced 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 signifi cance, i.e., business services, geospatial intelligence and brand marketing.