Knowledge Graphs (KG) are the backbone of many data-intensive applications since they can represent data coupled with its meaning and context. Aligning KGs across different domains and providers is necessary to afford a fuller and integrated representation. A severe limitation of current KG alignment (KGA) algorithms is that they fail to articulate logical thinking and reasoning with lexical, structural, and semantic data learning. Deep learning models are increasingly popular for KGA inspired by their good performance in other tasks, but they suffer from limitations in explainability, reasoning, and data efficiency. Hybrid neurosymbolic learning models hold the promise of integrating logical and data perspectives to produce high-quality alignments that are explainable and support validation through human-centric approaches. This paper examines the current state of the art in KGA and explores the potential for neurosymbolic integration, highlighting promising research directions for combining these fields.

翻译：知识图谱是许多数据密集型应用的支柱，因为它们能够表示数据及其含义和上下文。跨不同领域和提供商对齐知识图谱对于提供更全面和集成的表示是必要的。当前知识图谱对齐算法的一个严重局限性在于，它们未能将逻辑思维与推理同词汇、结构和语义数据学习相结合。受深度学习模型在其他任务中出色表现的启发，它们在知识图谱对齐中日益流行，但在可解释性、推理和数据效率方面存在局限。混合神经符号学习模型有望整合逻辑和数据视角，生成可解释且支持通过以人为中心的方法进行验证的高质量对齐。本文探讨了知识图谱对齐的最新研究现状，并探索了神经符号集成的潜力，指出了结合这些领域的有前景的研究方向。