Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations. Existing CLQA methods that learn parameters bound to certain entity or relation vocabularies can only be applied to the graph they are trained on which requires substantial training time before being deployed on a new graph. Here we present UltraQuery, an inductive reasoning model that can zero-shot answer logical queries on any KG. The core idea of UltraQuery is to derive both projections and logical operations as vocabulary-independent functions which generalize to new entities and relations in any KG. With the projection operation initialized from a pre-trained inductive KG reasoning model, UltraQuery can solve CLQA on any KG even if it is only finetuned on a single dataset. Experimenting on 23 datasets, UltraQuery in the zero-shot inference mode shows competitive or better query answering performance than best available baselines and sets a new state of the art on 14 of them.

翻译：复杂逻辑查询回答（CLQA）超越了简单的知识图谱（KG）补全任务，旨在回答由多个投影和逻辑操作组成的组合查询。现有CLQA方法学习与特定实体或关系词汇绑定的参数，只能应用于其训练所用的图谱，在部署到新图谱前需要大量的训练时间。本文提出UltraQuery，一种归纳推理模型，能够对任意知识图谱上的逻辑查询进行零样本回答。UltraQuery的核心思想是将投影和逻辑操作都推导为与词汇无关的函数，从而泛化到任意图谱中的新实体和关系。通过从预训练的归纳式知识图谱推理模型中初始化投影操作，UltraQuery即使在仅基于单个数据集微调的情况下，也能解决任意KG上的CLQA问题。在23个数据集上的实验表明，UltraQuery在零样本推理模式下展现出与最优基线方法竞争或更优的查询回答性能，并在其中14个数据集上实现了新的最优结果。