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xLP: Explainable Link Prediction Demo

Balaji Ganesan · Matheen Ahmed Pasha · Srinivasa Parkala · Neeraj R Singh · Gayatri Mishra · Sumit Bhatia · Hima Patel · Somashekar Naganna · Sameep Mehta


Explaining neural model predictions to users requires creativity. Especially in enterprise applications, where there are costs associated with users' time, and their trust in the model predictions is critical for adoption. For link prediction in master data management, we have built a number of explainability solutions drawing from research in interpretability, fact verification, path ranking, neuro-symbolic reasoning and self-explaining AI. In this demo, we present explanations for link prediction in a creative way, to allow users to choose explanations they are more comfortable with.

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