xLP：用于主数据管理的可解释链接预测
xLP: Explainable Link Prediction for Master Data Management
AI总结：
针对主数据管理中的链接预测，提出多种可解释性方案，并以创造性方式呈现解释，让用户选择更舒适的解释方式。
摘要：
向用户解释神经模型的预测需要创造性。尤其是在企业应用中，用户的时间成本高昂，且他们对模型预测的信任对于采用至关重要。针对主数据管理中的链接预测，我们基于可解释性、事实验证、路径排序、神经符号推理和自解释AI的研究，构建了多种可解释性解决方案。在本演示中，我们以创造性的方式呈现链接预测的解释，允许用户选择他们更满意的解释。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
