This chapter delves into the emerging field of neuro-symbolic query optimization for knowledge graphs (KGs), presenting a comprehensive exploration of how neural and symbolic techniques can be integrated to enhance query processing. Traditional query optimizers in knowledge graphs rely heavily on symbolic methods, utilizing dataset summaries, statistics, and cost models to select efficient execution plans. However, these approaches often suffer from misestimations and inaccuracies, particularly when dealing with complex queries or large-scale datasets. Recent advancements have introduced neural models, which capture non-linear aspects of query optimization, offering promising alternatives to purely symbolic methods. In this chapter, we introduce neuro-symbolic query optimizers, a novel approach that combines the strengths of symbolic reasoning with the adaptability of neural computation. We discuss the architecture of these hybrid systems, highlighting the interplay between neural and symbolic components to improve the optimizer's ability to navigate the search space and produce efficient execution plans. Additionally, the chapter reviews existing neural components tailored for optimizing queries over knowledge graphs and examines the limitations and challenges in deploying neuro-symbolic query optimizers in real-world environments.

翻译：本章深入探讨了知识图谱（KGs）中新兴的神经符号查询优化领域，全面阐述了如何整合神经与符号技术以增强查询处理能力。知识图谱中的传统查询优化器主要依赖符号方法，利用数据集摘要、统计信息和成本模型来选择高效执行计划。然而，这些方法在处理复杂查询或大规模数据集时，常常面临估计偏差和准确性问题。近期的研究进展引入了神经模型，这些模型能够捕捉查询优化的非线性特征，为纯符号方法提供了有前景的替代方案。在本章中，我们介绍神经符号查询优化器，这是一种将符号推理的优势与神经计算的适应性相结合的新颖方法。我们讨论了这些混合系统的架构，重点阐述了神经与符号组件之间的相互作用，以提升优化器在搜索空间中导航并生成高效执行计划的能力。此外，本章回顾了专为知识图谱查询优化而设计的现有神经组件，并探讨了在实际环境中部署神经符号查询优化器所面临的局限性与挑战。