利用多语言共享知识提升跨语言知识图谱补全效果
Multilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing
- 设计分组专家模型与迭代实体重排序，挖掘多语言间共享知识
- 在五种语言上实现Hits@1提升5.47%、Hits@3提升3.27%
- 适用于低资源语言和不平衡语种场景的知识补全任务
基于大语言模型的多语言知识图谱补全（MKGC）旨在利用大语言模型的多语言理解能力预测缺失事实，提升多语言知识图谱的完整性。然而，现有研究未能充分挖掘大语言模型的多语言能力，也忽视了跨语言知识的可共享性。本文提出一种新型MKGC框架，通过两个组件——知识级分组专家模型（KL-GMoE）和迭代实体重排序（IER），高效建模并增强跨语言共享知识的利用。为评估该框架，我们构建了一个包含5种语言的mKG数据集，并与当前最先进的MKGC方法进行对比实验。结果表明，相比SOTA方法，本框架在Hits@1、Hits@3和Hits@10指标上分别提升5.47%、3.27%和1.01%。进一步分析揭示了在未见语言和非均衡语言设置下知识共享的特性。代码与数据已开源至https://github.com/gaoxiaofei07/KL-GMoE。
原文摘要 · Abstract (English)
Large language models (LLMs) based Multilingual Knowledge Graph Completion (MKGC) aim to predict missing facts by leveraging LLMs' multilingual understanding capabilities, improving the completeness of multilingual knowledge graphs (KGs). However, existing MKGC research underutilizes the multilingual capabilities of LLMs and ignores the shareability of cross-lingual knowledge. In this paper, we propose a novel MKGC framework that leverages multilingual shared knowledge to significantly enhance performance through two components: Knowledge-level Grouped Mixture of Experts (KL-GMoE) and Iterative Entity Reranking (IER). KL-GMoE efficiently models shared knowledge, while IER significantly enhances its utilization. To evaluate our framework, we constructed a mKG dataset containing 5 languages and conducted comprehensive comparative experiments with existing state-of-the-art (SOTA) MKGC method. The experimental results demonstrate that our framework achieves improvements of 5.47%, 3.27%, and 1.01% in the Hits@1, Hits@3, and Hits@10 metrics, respectively, compared with SOTA MKGC method. Further experimental analysis revealed the properties of knowledge sharing in settings of unseen and unbalanced languages. We have released the dataset and code for our work on https://github.com/gaoxiaofei07/KL-GMoE.
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