@inproceedings{kyaw-etal-2026-graph,
title = "Graph Fusion across Languages Using Large Language Models",
author = "Kyaw, Kaung Myat and
Agarwal, Khush and
Chan, Jonathan",
editor = "S{\'e}rasset, Gilles and
Gkirtzou, Katerina and
Cochez, Michael and
Kalo, Jan-Christoph",
booktitle = "Proceedings of the Knowledge Graphs and Large Language Models Workshop ({KG}-{LLM}) @ {LREC}26",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.kallm-1.11/",
doi = "10.63317/3eka5ehdb339",
pages = "102--109",
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 and a new candidate graph. 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. Our implementation and experimental framework are publicly available in our repository: \url{https://github.com/IC2-Lab-KMUTT/Multilingual-Graph-Fusion}"
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<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 and a new candidate graph. 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. Our implementation and experimental framework are publicly available in our repository: https://github.com/IC2-Lab-KMUTT/Multilingual-Graph-Fusion</abstract>
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%0 Conference Proceedings
%T Graph Fusion across Languages Using Large Language Models
%A Kyaw, Kaung Myat
%A Agarwal, Khush
%A Chan, Jonathan
%Y Sérasset, Gilles
%Y Gkirtzou, Katerina
%Y Cochez, Michael
%Y Kalo, Jan-Christoph
%S Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F kyaw-etal-2026-graph
%X 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 and a new candidate graph. 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. Our implementation and experimental framework are publicly available in our repository: https://github.com/IC2-Lab-KMUTT/Multilingual-Graph-Fusion
%R 10.63317/3eka5ehdb339
%U https://aclanthology.org/2026.kallm-1.11/
%U https://doi.org/10.63317/3eka5ehdb339
%P 102-109
Markdown (Informal)
[Graph Fusion across Languages Using Large Language Models](https://aclanthology.org/2026.kallm-1.11/) (Kyaw et al., KaLLM 2026)
ACL
- Kaung Myat Kyaw, Khush Agarwal, and Jonathan Chan. 2026. Graph Fusion across Languages Using Large Language Models. In Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26, pages 102–109, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).