Multilingual knowledge graphs (KGs) provide high-quality relational and textual information for various NLP applications, but they are often incomplete, especially in non-English languages. Previous research has shown that combining information from KGs in different languages aids either Knowledge Graph Completion (KGC), the task of predicting missing relations between entities, or Knowledge Graph Enhancement (KGE), the task of predicting missing textual information for entities. Although previous efforts have considered KGC and KGE as independent tasks, we hypothesize that they are interdependent and mutually beneficial. To this end, we introduce KG-TRICK, a novel sequence-to-sequence framework that unifies the tasks of textual and relational information completion for multilingual KGs. KG-TRICK demonstrates that: i) it is possible to unify the tasks of KGC and KGE into a single framework, and ii) combining textual information from multiple languages is beneficial to improve the completeness of a KG. As part of our contributions, we also introduce WikiKGE10++, the largest manually-curated benchmark for textual information completion of KGs, which features over 25,000 entities across 10 diverse languages.

本研究解决了多语言知识图谱在非英语语言下信息不完整的问题。我们提出了一种新颖的KG-TRICK框架，将关系信息补全和文本信息补全任务统一为一个整体，研究结果表明，来自多语言的文本信息的结合显著提高了知识图谱的完整性。此外，我们推出了WikiKGE10++数据集，作为知识图谱文本信息补全的最大手工整理基准。

KG-TRICK：统一多语言知识图谱的文本和关系信息补全