Complex logical query answering is a challenging task in knowledge graphs (KGs) that has been widely studied. The ability to perform complex logical reasoning is essential and supports various graph reasoning-based downstream tasks, such as search engines. Recent approaches are proposed to represent KG entities and logical queries into embedding vectors and find answers to logical queries from the KGs. However, existing proposed methods mainly focus on querying a single KG and cannot be applied to multiple graphs. In addition, directly sharing KGs with sensitive information may incur privacy risks, making it impractical to share and construct an aggregated KG for reasoning to retrieve query answers. Thus, it remains unknown how to answer queries on multi-source KGs. An entity can be involved in various knowledge graphs and reasoning on multiple KGs and answering complex queries on multi-source KGs is important in discovering knowledge cross graphs. Fortunately, federated learning is utilized in knowledge graphs to collaboratively learn representations with privacy preserved. Federated knowledge graph embeddings enrich the relations in knowledge graphs to improve the representation quality. However, these methods only focus on one-hop relations and cannot perform complex reasoning tasks. In this paper, we apply federated learning to complex query-answering tasks to reason over multi-source knowledge graphs while preserving privacy. We propose a Federated Complex Query Answering framework (FedCQA), to reason over multi-source KGs avoiding sensitive raw data transmission to protect privacy. We conduct extensive experiments on three real-world datasets and evaluate retrieval performance on various types of complex queries.

翻译：复杂逻辑查询回答是知识图谱中一项具有挑战性的任务，已得到广泛研究。执行复杂逻辑推理的能力至关重要，并支持多种基于图推理的下游任务，例如搜索引擎。近年来，研究者提出将知识图谱实体和逻辑查询表示为嵌入向量，并从知识图谱中查找逻辑查询的答案。然而，现有方法主要针对单一知识图谱进行查询，无法应用于多个图谱。此外，直接共享包含敏感信息的知识图谱可能带来隐私风险，使得共享和构建聚合知识图谱以进行推理和检索查询答案变得不切实际。因此，如何在多源知识图谱上回答查询仍是一个未解问题。一个实体可能涉及多个知识图谱，在多源知识图谱上进行推理和回答复杂查询对于发现跨图知识至关重要。幸运的是，联邦学习已被用于知识图谱中以在保护隐私的前提下协同学习表示。联邦知识图谱嵌入通过丰富知识图谱中的关系来提高表示质量。然而，这些方法仅关注单跳关系，无法执行复杂推理任务。本文中，我们将联邦学习应用于复杂查询回答任务，以在多源知识图谱上进行推理同时保护隐私。我们提出了一种联邦复杂查询回答框架（FedCQA），用于在多源知识图谱上进行推理，避免传输敏感原始数据以保护隐私。我们在三个真实数据集上进行了大量实验，并评估了各种复杂查询类型的检索性能。