CLRN: A reasoning network for multi-relation question answering over Cross-lingual Knowledge Graphs

Abstract

Cross-lingual Knowledge Graphs-based Question Answering (CLKGQA) requires the question answering (QA) system to combine the knowledge graphs (KGs) in different languages to obtain answers to input questions. In previous works, the common idea is to merge Cross-lingual Knowledge Graphs (CLKGs) into a single KG through aligned entity pairs and then treat it as a traditional KG-based QA. However, as demonstrated by Tan et al. (2023), existing Entity Alignment (EA) models cannot generate highly accurate aligned entity pairs for CLKGs. Therefore, two issues need to be addressed in the CLKGQA task: (1) Remove the dependency of the QA model on the fused KG; (2) Improve the performance of the EA model in obtaining aligned entity pairs from locally isomorphic CLKGs. To solve the above two issues, this paper presents Cross-lingual Reasoning Network (CLRN), a novel multi-hop QA model that allows switching knowledge graphs at any stage of the multi-hop reasoning. Furthermore, we establish an iterative framework that combines CLRN and EA model, in which CLRN is used for extracting potential alignment triple pairs from CLKGs during the QA process. The extracted triple pairs provide pseudo-aligned entities, and the additional aligned entity pairs are used to mine missing relations between entities in CLKGs. These pseudo-aligned entity pairs and relations improve the performance of the EA model, resulting in higher accuracy in QA. Extensive experiments demonstrate the effectiveness of the proposed model, which outperforms the baseline approaches. Through iterative enhancement, the performance of the EA model has also been improved by > 1.0 % in [email protected] and [email protected], and the improvement is statistically significant in the confidence interval of p<0.01. Moreover, our work discusses the correlation between QA and EA from the side of QA, which has reference value for the follow-up exploration of related communities. We have open-sourced our dataset and code, which is available at the URL https://github.com/tan92hl/Cross-lingual-Reasoning-Network-for-CLKGQA.