Kelpie: an Explainability Framework for Embedding-based Link Prediction Models

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Abstract

The latest generations of Link Prediction (LP) models heavily rely on embeddings to tackle incompleteness in Knowledge Graphs, achieving great performance at the cost of interpretability. Their opaqueness limits the trust that users can place in these systems, and hinders their adoption in real-world applications. We have recently introduced Kelpie, an explainability framework tailored specifically for embedding-based LP models. Kelpie can be applied to any embedding-based LP model, and supports two explanation scenarios that we have called necessary and sufficient. In this demonstration we showcase Kelpie’s capability to explain the predictions of models based on vastly different architectures on the 5 major datasets in literature.