xLP: Explainable Link Prediction for Master Data Management

Autor:	Ganesan, Balaji, Pasha, Matheen Ahmed, Parkala, Srinivasa, Singh, Neeraj R, Mishra, Gayatri, Bhatia, Sumit, Patel, Hima, Naganna, Somashekar, Mehta, Sameep
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 4 figures, NeurIPS 2020 Competition and Demonstration Track. arXiv admin note: text overlap with arXiv:2012.05516
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.09806 Zobrazit plný text záznamu View this record from Arxiv