In real-world industrial settings, companies typically maintain independent knowledge bases due to privacy regulations and business confidentiality constraints. This leads to severe data silo issues, hindering cross-organizational knowledge integration and reasoning. Federated knowledge graph completion (FKGC) with multi-client collaboration has emerged as a promising solution to address the aforementioned data silo problem. However, existing methods primarily rely on graph structure while neglecting the rich semantics encoded in the textual descriptions of entities and relations, thereby limiting the performance of knowledge graph completion. To address this limitation, we propose an FKGC method named LLMST, which uses large language models (LLMs) to integrate the knowledge graph structural information and the rich textual information of entities and relations. Specifically, to alleviate the computational burden on clients, the LLM is partitioned into two components: a lightweight neural embedding synthesizer, which is deployed on both clients and the server, and a computation-intensive contextual inference engine, which runs exclusively on the server. Each client locally collects structural paths and integrates them with textual information. Then, the server aggregates the information uploaded by all clients to score the query triple. The experimental results show that the proposed LLMST method outperforms baseline models across multiple datasets and evaluation metrics, demonstrating the effectiveness of integrating structural and textual information.
Liu et al. (Fri,) studied this question.