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.

利用联邦学习在多源知识图谱上进行复杂查询回答任务，避免敏感原始数据传输以保护隐私。