m RAKL : Multilingual Retrieval-Augmented Knowledge Graph Construction for Low-Resourced Languages (2025)

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, to utilize cross-lingual transfer for mKGC. 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 up to 4.92 and 8.79 percentage points for Tigrinya and Amharic, respectively.

## introduction knowledge graphs (kg) are structured multirelational graphs that store factual knowledge. in a kg, nodes represent entities (e.g., michelle obama, sasha obama) and links represent relationships between the nodes (e.g., michelle obama -mother -sasha obama). multilingual kgs are kgs in multiple languages. despite their myriad downstream applications, including question answering @xcite , information retrieval @xcite , and language model augmentation @xcite , most kgs are incomplete @xcite . the quantity of missing information in kgs is even greater in low-resourced languages @xcite . additionally, manual construction of kgs is expensive @xcite . recent work has investigated the use of pre-trained language models (lms) for kg construction (e.g. @xcite . however, most of the work is focused on english, for which lms have good performance @xcite . multilingual knowledge graph construction (mkgc) research allows us to (1) extend the downstream benefits of kgs to multiple languages, and (2) capture culturally nuanced and relevant information across languages. however, the challenges of mkgc are exacerbated for languages with limited data available. prior work using lms for mkgc relies on pre-training lms with large amounts of structured data (e.g., @xcite train on a kg with 52m triples). however, languages on the long tail do not have such datasets available @xcite . based on official statistics, only 0.2% of the total entities in wikidata @xcite have labels in the lowresourced language amharic. foot_0 additionally, most pre-trained lms do not have good performance for low-resourced languages @xcite . we propose mrakl, a retrieval-augmented sequence-to-sequence generative method for mkgc. mrakl uses a retriever which fetches relevant passages and passes them to a generator model which predicts knowledge facts. moreover, we allow lms to learn better cross-lingual entity representation leveraging entity parallel textual information from wikidata @xcite . these two approaches greatly alleviate the data scarcity problem. we focus on enriching kgs of two low-resourced languages: amharic and tigrinya, two afro-semitic languages. r d lant reformulated data g ሱራፌል ዳኛቸው ...ኢትዮጵያዊ ፕሮፌሽናል እግር ኳስ ተጫዋች [c-amh] ሱራፌል ዳኛቸው...ኢትዮጵያዊ ፕሮፌሽናል ... | [q-amh]የሱራፌል ዳኛቸው የትውልድ ቦታ የት ነው? [a-tir] tigrinya ‫اﻟﻮﻃﻨﻲ‬ ‫إﺛﻴﻮﺑﻴﺎ‬ ‫وﻣﻨﺘﺨﺐ‬ ‫إﺛﻴﻮﺑﻲ...‬ ‫ﻫﻮ‬ [c-ara] ‫اﻟﻮﻃﻨﻲ...‬ ‫إﺛﻴﻮﺑﻴﺎ‬ ‫...وﻣﻨﺘﺨﺐ‬ | [qara] ‫؟‬ ‫داﺟﻨﺎﺷﻴﻮ‬ ‫ﺳﻮراﻓﻴﻞ‬ ‫وﻻدة‬ ‫ﻣﻜﺎن‬ ‫ﻫﻮ‬ ‫ﻣﺎ‬ [a-tir] [c-tir] ክርስቶስ ቦታ ልደት | [q-tir] ናይ ሱራፊኤል ዳግናቸው ቦታ ልደት ኣበይ እዩ? [a-tir] [c-eng] ...was born in ethiopia | [qeng] what is surafel dagnachew's place of birth? [a-tir] a lan t ኢትዮጵያ surafel dagnachew ... was born in ethiopia ክርስቶስ ቦታ ልደት c lan t tigrinya english amharic arabic wiki @xmath0: we construct a question text that uses the head entity and relation from the kg triple and an answer text that uses the tail (see figure 8 ). for our generator model, we finetune a multilingual lm, afriteva (jude ogundepo et al., 2022) with cross-lingual entity-centered alignment data. this finetuning allows the model to learn better representations for the entities in the low-resourced languages and unify complementary knowledge across languages. since data in these languages is small, we train a retriever model to further enrich the construction by the generator, utilizing monolingual datasets which are more easily available for low-resourced languages compared to structured, labeled datasets @xcite we evaluate our approach and existing approaches on two tasks: probing parametric knowledge of pre-trained lms for low-resourced languages ( §4.2) and monolingual and cross-lingual link prediction ( §4.4). we also perform ablation studies showing the benefit of rag for mkgc where structured data is constrained ( §a.2). overall, we find that mrakl outperforms prior approaches on amharic and tigrinya by an 8 and 6 percentage point increase and that using crosslingual context improves over no-context settings. our contributions can be summarized as follows: a number of multilingual kg embedding-based approaches have been proposed to tackle the crosslingual knowledge alignment and kg completion problems (e.g @xcite @xcite . the key idea of these approaches is to align the knowledge across kgs in different languages into a unified embedding space so that the link prediction can leverage the complementary knowledge @xcite . nevertheless, these approaches assume a closedworld framework which cannot take open-world information and natural language knowledge into consideration. our system integrates the best of the worlds by combining knowledge from multilingual open-domain text through rag and multilingual kgs through cross-entity alignment. retrieval augmented generation rag augments llms with non-parametric memories from one or more external data sources. rag has attracted significant attention recently because it addresses several critical limitations of llms (e.g. @xcite @xcite ; for example, it is expensive to correct outdated, erroneous facts within llms through finetuning or re-training. rag offers a way to circumvent this issue by providing up-to-date information to a pre-trained model during generation. furthermore, rag enables llms to focus on generalization and reasoning which reduces the size of llms needed to achieve similar performance @xcite . given that low-resource languages occupy the long tail of available knowledge during the training phase of llms, rag offers a way to boost the performance for mkgc by retrieving new, open-domain information instead of inferring knowledge only from multilingual kgs. while several research works focus on augmenting llms with @xcite , to the best of our knowledge mrakl is the first work to explore how rag with llms can help construct and complete kgs in a low-resourced setting. ## proposed method our proposed method is to reformulate mkgc as a cross-lingual question-answering task and use an rag-based qa system for completion. we first convert the (head, relation, tail) triples into @xmath1, @xmath2.1). using a retriever model, we extract context from monolingual unlabeled datasets. we then use the generator model to predict the tail entity, given the extracted context and the question (figure 1 ). below we first give background on our languages of focus, then detail our data preparation steps ( §3.1) and then present mrakl ( §3.2). languages of study our main languages of study are tigrinya and amharic, afro-semitic languages that use the ge'ez script. the languages are considered low-resourced in that there are limited tools and data available to build language technologies for them @xcite . tigrinya is one of the official languages of ethiopia and eritrea and is spoken by 9.7 million people foot_1 in total across the two countries and their diasporas. amharic is one of the official languages of ethiopia, spoken by over 33.7 million people as a first language and 25.1 million as a second language @xcite . to enrich the data for these languages, we chose two transfer languages: arabic and english. we selected arabic as a transfer language because (1) it is in the same language family as tigrinya and amharic (motivated by @xcite )and (2) we hypothesize there are cultural and geographic ties that would make the information more culturally relevant (motivated by @xcite ). we select english as a second transfer language because of the abundance of resources in the language. ## experiments and results in this section, we evaluate our proposed method with two tasks: (1) parametric knowledge probing, which is a way to test knowledge representations in an lm's learned embeddings ( §4.2) and (2) link prediction, which is a standard task for evaluating kg completion and construction. for link prediction, we evaluate by (1) comparing our method with that of prior work approaches ( §4.3) and ( 2 ) by looking at cross-lingual link prediction, where we predict the tail entity in one language given a head and relation entity in another language ( §4.4). we also perform additional analysis with our (im)perfect retriever. overall, we find that our approach outperforms prior methods by over a 4.9 percentage point increase and that providing context improves over a no-context setting by 6.7 and 12.22 percentage point increase. we also find that multilingual and cross-lingual approaches are more beneficial for tigrinya, where the data is limited and the transfer languages cover the majority of the entities in the target language (see §c). all results reported are based on a single inference run. ## conclusion we propose mrakl, a rag-based approach for mkgc in low-resourced language settings. we have shown that a rag-based finetuned lm can retrieve facts that help with mkgc. in addition, our cross-lingual entity alignment technique combines complementary knowledge across languages and increases the available corpus for rag. our experimental results demonstrate that mrakl increases accuracy by up to 4.92 and 8.79 percentage points for tigrinya and amharic, respectively, compared with baselines. our approach represents a step towards alleviating the cultural knowledge scarcity that llms typically display during pre-training.