Graph Fusion Across Languages using Large Language Models

2026-03-22

AI Summary

This work proposes a sequential fusion approach based on large language models (LLMs) to address the challenges of integrating cross-lingual knowledge graphs, which suffer from semantic heterogeneity and structural complexity. By leveraging LLMs as a universal semantic bridge for the first time, the method linearizes knowledge graph triples into natural language sequences and integrates cross-lingual entity alignment with relation mapping to enable modular, scalable, and continuous fusion of multi-source heterogeneous graphs. Experimental results on the DBP15K dataset demonstrate that the proposed approach effectively supports sequential aggregation of multilingual knowledge graphs while achieving strong performance and scalability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Combining multiple knowledge graphs (KGs) across linguistic boundaries is a persistent challenge due to semantic heterogeneity and the complexity of graph environments. We propose a framework for cross-lingual graph fusion, leveraging the in-context reasoning and multilingual semantic priors of Large Language Models (LLMs). The framework implements structural linearization by mapping triplets directly into natural language sequences (e.g., [head] [relation] [tail]), enabling the LLM to map relations and reconcile entities between an evolving fused graph ($G_{c}^{(t-1)}$) and a new candidate graph ($G_{t}$). Evaluated on the DBP15K dataset, this exploratory study demonstrates that LLMs can serve as a universal semantic bridge to resolve cross-lingual discrepancies. Results show the successful sequential agglomeration of multiple heterogeneous graphs, offering a scalable, modular solution for continuous knowledge synthesis in multi-source, multilingual environments.

Problem

• knowledge graph fusion
• cross-lingual
• semantic heterogeneity
• multilingual knowledge integration
• graph alignment

Innovation

• cross-lingual graph fusion
• large language models
• structural linearization
• knowledge graph alignment
• multilingual semantic bridging