Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning. The primary obstacle in this task stems from the inherent incompleteness of KGs. Existing research has predominantly focused on addressing the issue of missing edges in KGs, thereby neglecting another aspect of incompleteness: the emergence of new entities. Furthermore, most of the existing methods tend to reason over each logical operator separately, rather than comprehensively analyzing the query as a whole during the reasoning process. In this paper, we propose a query-aware prompt-fused framework named Pro-QE, which could incorporate existing query embedding methods and address the embedding of emerging entities through contextual information aggregation. Additionally, a query prompt, which is generated by encoding the symbolic query, is introduced to gather information relevant to the query from a holistic perspective. To evaluate the efficacy of our model in the inductive setting, we introduce two new challenging benchmarks. Experimental results demonstrate that our model successfully handles the issue of unseen entities in logical queries. Furthermore, the ablation study confirms the efficacy of the aggregator and prompt components.

翻译：在知识图谱（KG）上进行逻辑查询回答对机器推理构成重大挑战。该任务的主要障碍源于知识图谱固有的不完整性。现有研究主要关注知识图谱中缺失边的问题，从而忽略了不完整性的另一方面：新实体的出现。此外，大多数现有方法倾向于单独推理每个逻辑运算符，而非在推理过程中将查询作为一个整体进行全面分析。本文提出一种查询感知的提示融合框架Pro-QE，该框架能够融合现有查询嵌入方法，并通过上下文信息聚合处理新兴实体的嵌入。同时，引入一种通过编码符号查询生成的查询提示，从整体角度收集与查询相关的信息。为评估模型在归纳设置下的有效性，我们引入了两个新的具有挑战性的基准测试。实验结果表明，我们的模型成功处理了逻辑查询中未见实体的问题。此外，消融研究证实了聚合器和提示组件的有效性。