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Recent work in Natural Language Processing and Computer Vision has been usingtextual information -- e.g., entity names and descriptions -- available inknowledge graphs to ground neural models to high-quality structured data.However, when it comes to non-English languages, the quantity and quality oftextual information are comparatively scarce. To address this issue, weintroduce the novel task of automatic Knowledge Graph Enhancement (KGE) andperform a thorough investigation on bridging the gap in both the quantity andquality of textual information between English and non-English languages. Morespecifically, we: i) bring to light the problem of increasing multilingualcoverage and precision of entity names and descriptions in Wikidata; ii)demonstrate that state-of-the-art methods, namely, Machine Translation (MT),Web Search (WS), and Large Language Models (LLMs), struggle with this task;iii) present M-NTA, a novel unsupervised approach that combines MT, WS, andLLMs to generate high-quality textual information; and, iv) study the impact ofincreasing multilingual coverage and precision of non-English textualinformation in Entity Linking, Knowledge Graph Completion, and QuestionAnswering. As part of our effort towards better multilingual knowledge graphs,we also introduce WikiKGE-10, the first human-curated benchmark to evaluate KGEapproaches in 10 languages across 7 language families.

Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs