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，如搜索引擎。现有方法通过将知识图谱实体和逻辑查询表示为嵌入向量，从知识图谱中寻找逻辑查询的答案。然而，这些方法主要集中于查询单一知识图谱，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，使得共享并构建聚合知识图谱进行推理以检索查询答案变得不可行。因此，如何回答多源知识图谱上的查询仍是一个未知问题。一个实体可能涉及多个知识图谱，对多图谱进行推理并回答复杂查询，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作学习表示。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），该框架可避免传输包含敏感信息的原始数据，从而在多源知识图谱上进行推理并保护隐私。我们在三个真实世界数据集上进行了广泛实验，并评估了多种类型复杂查询的检索性能。