用问答方式提升低资源语言知识图谱构建效果
mRAKL: Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages
- 将知识图谱补全转化为问答任务，利用检索增强生成
- 在提格雷尼亚和阿姆哈拉语上分别提升8.79%和4.92%准确率
- 适合低资源语言知识图谱研究者参考
知识图谱表示现实世界中的实体及其相互关系。多语言知识图谱构建（mKGC）是在多语言环境下自动构建或预测缺失实体与链接的任务。本文将mKGC重新定义为问答任务，提出基于检索增强生成（RAG）的mRAKL系统：以头实体和关系作为问题，让模型预测尾实体作为答案。实验聚焦于提格雷尼亚语和阿姆哈拉语两种低资源语言，并尝试使用阿拉伯语和英语进行跨语言迁移。采用BM25检索器时，RAG方法显著优于无上下文设置。消融实验表明，在理想检索条件下，mRAKL在提格雷尼亚语和阿姆哈拉语上的准确率分别提升4.92和8.79个百分点。
原文摘要 · Abstract (English)
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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