David Jaime Tena Cucala — accepted AI-conference papers

2024

Faithful Rule Extraction for Differentiable Rule Learning Models

Xiaxia Wang, David Jaime Tena Cucala, Bernardo Cuenca Grau, Ian Horrocks

ICLR 2024poster

There is increasing interest in methods for extracting interpretable rules from ML models trained to solve a wide range of tasks over knowledge graphs (KGs), such as KG completion, node classification, question answering and recommendation. Many such approaches, however, lack formal guarantees estab…

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2022

Explainable GNN-Based Models over Knowledge Graphs

David Jaime Tena Cucala, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik

ICLR 2022poster

Graph Neural Networks (GNNs) are often used to learn transformations of graph data. While effective in practice, such approaches make predictions via numeric manipulations so their output cannot be easily explained symbolically. We propose a new family of GNN-based transformations of graph data that…

Cited by 41 Source ↗PDF ↗Scholar ↗