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，它利用自监督神经网络（即数据库嵌入）在关系表上实现语义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、透明且可关联的洞察。我们提出了一种基于共现的全新可解释性方法，以捕获关系实体之间的关联，并描述了一种空间高效的概率性Sketch实现，用于存储和处理共现计数。该方法支持查询无关（全局）和查询特定（局部）两种可解释性。实验评估表明，这种数据库内概率性方法在提供与精确但空间低效方法相同的可解释性质量的同时，实现了可扩展且空间高效的运行时性能（最高节省8倍空间），且无需任何用户干预。