mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages

• URL: http://arxiv.org/abs/2507.16011v1
• Date: Mon, 21 Jul 2025 19:11:31 GMT
• Title: mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages
• Authors: Hellina Hailu Nigatu, Min Li, Maartje ter Hoeve, Saloni Potdar, Sarah Chasins,
• Abstract summary: We introduce mRAKL: a Retrieval-Augmented Generation (RAG) based system to perform Multilingual Knowledge Graph Construction (mKGC)<n>Our experiments focus primarily on two low-resourced languages: Tigrinya and Amharic.<n>With an idealized retrieval system, mRAKL improves accuracy by 4.92 and 8.79 percentage points for Tigrinya and Amharic, respectively.
• Score: 9.920621166617925
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Knowledge Graphs represent real-world entities and the relationships between them. Multilingual Knowledge Graph Construction (mKGC) refers to the task of automatically constructing or predicting missing entities and links for knowledge graphs in a multilingual setting. In this work, we reformulate the mKGC task as a Question Answering (QA) task and introduce mRAKL: a Retrieval-Augmented Generation (RAG) based system to perform mKGC. We achieve this by using the head entity and linking relation in a question, and having our model predict the tail entity as an answer. Our experiments focus primarily on two low-resourced languages: Tigrinya and Amharic. We experiment with using higher-resourced languages Arabic and English for cross-lingual transfer. With a BM25 retriever, we find that the RAG-based approach improves performance over a no-context setting. Further, our ablation studies show that with an idealized retrieval system, mRAKL improves accuracy by 4.92 and 8.79 percentage points for Tigrinya and Amharic, respectively.

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