AI-Powered database (AI-DB) is a novel relational database system that uses a self-supervised neural network, database embedding, to enable semantic SQL queries on relational tables. In this paper, we describe an architecture and implementation of in-database interpretability infrastructure designed to provide simple, transparent, and relatable insights into ranked results of semantic SQL queries supported by AI-DB. We introduce a new co-occurrence based interpretability approach to capture relationships between relational entities and describe a space-efficient probabilistic Sketch implementation to store and process co-occurrence counts. Our approach provides both query-agnostic (global) and query-specific (local) interpretabilities. Experimental evaluation demonstrate that our in-database probabilistic approach provides the same interpretability quality as the precise space-inefficient approach, while providing scalable and space efficient runtime behavior (up to 8X space savings), without any user intervention.

翻译：AI赋能数据库（AI-DB）是一种新型关系数据库系统，其利用自监督神经网络（即数据库嵌入）实现关系表上的语义SQL查询。本文描述了一种数据库内可解释性基础设施的架构与实现，旨在为AI-DB支持的语义SQL查询的排序结果提供简单、透明且可关联的见解。我们提出了一种基于共现的新型可解释性方法，用于捕捉关系实体间的关联，并描述了一种空间高效的概率草图实现，以存储和处理共现计数。该方法同时支持查询无关（全局）与查询特定（局部）的可解释性。实验评估表明，我们的数据库内概率方法在保持与精确但空间低效方法相同可解释性质量的同时，展现出可扩展且空间高效的运行时特性（最高节省8倍空间），且无需任何用户干预。