mlsanityData to 14 Oct 2026

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

arXiv 2507.16011 ↗First posted 21 Jul 2025cs.CLcited by 0

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 55 references that are in the map. First posted on arXiv 21 Jul 2025.

Builds on

6 of 55 references.

References · 6

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2019KG-BERT: BERT for Knowledge Graph Completioncited by 324
  5. 2024BM25S: Orders of magnitude faster lexical search via eager sparse scoringcited by 88
  6. 2023AfroBench: How Good are Large Language Models on African Languages?cited by 43

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  1. 2026Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering
  2. 2026UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
  3. 2025Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
  4. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
  5. 2025Knowledge Graph-extended Retrieval Augmented Generation for Question Answering
  6. 2025On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation
  7. 2026MKG-RAG-Bench: Benchmarking Retrieval in Multimodal Knowledge Graph-Augmented Generation
  8. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation