mRAKL：面向低资源语言的多语言检索增强知识图谱构建
mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages
AI总结：
本文提出mRAKL系统，将多语言知识图谱构建重构为问答任务，结合检索增强生成技术，在提格里尼亚语和阿姆哈拉语等低资源语言上显著提升链接预测准确率。
摘要：
知识图谱表示现实世界中的实体及其之间的关系。多语言知识图谱构建（mKGC）是指在多语言环境下自动构建或预测知识图谱中缺失实体和链接的任务。在本工作中，我们将mKGC任务重新表述为问答（QA）任务，并引入mRAKL：一个基于检索增强生成（RAG）的系统来执行mKGC。我们通过在问题中使用头实体和链接关系，并让模型预测尾实体作为答案来实现这一目标。我们的实验主要关注两种低资源语言：提格里尼亚语和阿姆哈拉语。我们尝试使用资源较丰富的语言阿拉伯语和英语进行跨语言迁移。使用BM25检索器，我们发现基于RAG的方法相比无上下文设置提升了性能。此外，我们的消融研究表明，在理想化的检索系统下，mRAKL在提格里尼亚语和阿姆哈拉语上分别将准确率提高了4.92和8.79个百分点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
