mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913952224509952 |
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| author | Nigatu, Hellina Hailu Li, Min ter Hoeve, Maartje Potdar, Saloni Chasins, Sarah |
| author_facet | Nigatu, Hellina Hailu Li, Min ter Hoeve, Maartje Potdar, Saloni Chasins, Sarah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages Nigatu, Hellina Hailu Li, Min ter Hoeve, Maartje Potdar, Saloni Chasins, Sarah Computation and Language 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. |
| title | mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.16011 |