Abstract
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.
Abstract (translated)
复杂逻辑查询回答是在知识图谱（KG）中具有挑战性的任务，已经被广泛研究。实现复杂逻辑推理的能力是必不可少的，并支持各种基于图推理的下游任务，如搜索引擎。为了将KG实体和逻辑查询表示为嵌入向量，并从KG中找到逻辑查询的答案，近年来提出了许多方法。然而，现有的方法主要集中在查询单个KG，不能应用于多个图形。此外，直接共享KG可能会导致隐私风险，使得分享和构建汇总KG来推理以检索查询答案变得不切实际。因此，回答多源KG的查询仍然是一个未知的问题。实体可以参与各种知识图和在不同KG上的推理，并在多源KG上回答复杂查询对于发现知识跨图网络非常重要。幸运的是，在知识图中使用了联邦学习来协同学习具有隐私保护的表示。联邦知识图嵌入丰富知识图中的关系，提高表示质量。然而，这些方法仅关注一跳关系，不能执行复杂的推理任务。在本文中，我们将联邦学习应用于复杂查询回答任务，在保留隐私的同时推理多源知识图。我们提出了一个联邦复杂查询回答框架（FedCQA），以在多源KG上进行推理，并避免敏感原始数据传输以保护隐私。我们对三个现实世界的数据集进行了广泛的实验，并评估了各种复杂查询的检索性能。
URL
https://arxiv.org/abs/2402.14609
