Multilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing (2025)

Abstract: 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.

## introduction knowledge graphs (kgs) @xcite are structured semantic knowledge bases designed to represent and organize knowledge about the real world. most kgs possess multilingual characteristics, including wikidata (vrandečić and krötzsch, * corresponding author studies have focused on embedding-based methods @xcite for mkgc, mapping entities and relations into a low-dimensional vector space to achieve completion. recent advances in language models have shifted mkgc research toward generation-based approaches @xcite that reformulate kg comple-tion as text generation. methods @xcite employ a single pretrained language model (plm) to consolidate multilingual knowledge within a unified semantic space, achieving superior performance in mkgc. furthermore, recent work like dift @xcite explores task adaptation through llms fine-tuning, achieving strong performance on monolingual kgc tasks. modern llms, pretrained on diverse corpora, inherently possess multilingual capabilities @xcite , enabling the representation and knowledge sharing across languages @xcite within a unified model. crucially, this capacity for internal multilingual knowledge sharing is vital for mkgc, offering substantial potential to enhance completion performance. motivated by this recognized potential, our research investigates effective methods for harnessing llms' inherent capabilities to improve mkgc. however, directly applying llms to mkgc presents several challenges, primarily stemming from two key aspects: model architecture and task paradigm. 1) architectural mismatch: existing parameter-efficient fine-tuning (peft) methods @xcite for llms are mainly designed for text-centric tasks and exhibit significant gaps when applied to knowledge-level tasks. specifically, single-channel methods struggle with the complex multilingual nature of kgs. as shown in figure 1 (a) top, processing numerous multilingual queries through a single channel often results in knowledge overload. this overload impacts the model's ability to understand similar knowledge across languages, leading to incorrect predictions. for example, queries in english, chinese, and italian concerning einstein's birthplace all yield incorrect results. conversely, multi-channel methods tend to disrupt the atomicity of knowledge, thereby causing knowledge fragmentation. as shown in figure 1 (a) bottom, query tokens are processed by disparate channels. such fragmented processing consequently leads to incorrect entity predictions. 2) task paradigm discrepancy: the mkgc task involves entity ranking, which presents a discrepancy with the text generation paradigm. as shown in figure 1 (b), for the query (einstein, birthplace, ?), the llm erroneously predicted paris as the answer. this selection failed to improve the ranking of the correct entity ulm. to address the 1) architectural mismatch, specifically knowledge overload, we propose increasing the number of dedicated knowledge chan-nels. this allows each channel to focus on processing semantically similar information, thereby enhancing the llm's capacity to understand and leverage cross-lingual shared knowledge. concurrently, by enabling each channel to independently process complete knowledge, we can effectively mitigate knowledge fragmentation and facilitate the model's comprehensive understanding of multilingual information. to resolve the 2) task paradigm discrepancy, we propose adjusting the llm's training objective to enable it to iteratively refine the ranking of multiple entities. this approach aims to enhance the ranking of correct entities by increasing the frequency with which the llm utilizes cross-lingual shared knowledge. in this paper, we propose a novel framework for effectively leveraging multilingual shared knowledge to enhance the performance of mkgc. this proposed framework comprises two synergistic components: knowledge-level grouped mixture of experts (kl-gmoe) and iterative entity reranking (ier). kl-gmoe introduces a knowledgelevel expert routing mechanism and a group-based mixture-of-experts (moe) architecture. this design aims to mitigate knowledge fragmentation while substantially enhancing llms' capacity to capture cross-lingual shared knowledge. ier modifies both the training objective and the decoding strategy of llms. this enables the models to significantly improve their leveraging of multilingual shared knowledge through multiple iterative refinements. 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. in summary, our contributions are as follows: ## related work embedding-based methods map entities and relations in kgs to low-dimensional vector spaces. for example, transe @xcite based on the translation principle of entities and relations. rotate @xcite treats each relation as rotation in complex vector space. dmog @xcite represents the unseen relations of the factual graph by fusing ontology and textual graphs. transh @xcite models relation as hyperplane. holex @xcite interpolate between a high model complexity method and hole @xcite . tr-gcn @xcite proposes an ontology-guided zeroshot relation learning method to represent unseen relations. generation-based methods transform kgc task into text generation task. for example, kgt5 @xcite posing kg link prediction as a sequence-to-sequence task. gc-plm @xcite enhances the performance of mkgc by introducing global and local knowledge constraints. genkgc @xcite introduces a hierarchical decoding strategy of relation-guided demonstration and entity awareness. kicgpt @xcite integrates llms and kge model, adopting a knowledge-prompted contextual learning strategy to rerank multiple entities. dift @xcite fine-tunes llms using lora @xcite to select the most optimal entity from candidate entities obtained by the kge model. ## conclusion in this paper, we propose a novel mkgc framework. this framework integrates two components: kl-gmoe and ier. kl-gmoe significantly improves completion performance by efficiently capturing shared knowledge across languages. ier fully utilized cross-lingual shared knowledge through a multi-round iterative approach, further improving completion performance. the experimental results demonstrate that our framework exhibits superior performance in the mkgc task.