  Recent work in Natural Language Processing and Computer Vision has been using
textual information -- e.g., entity names and descriptions -- available in
knowledge graphs to ground neural models to high-quality structured data.
However, when it comes to non-English languages, the quantity and quality of
textual information are comparatively scarce. To address this issue, we
introduce the novel task of automatic Knowledge Graph Enhancement (KGE) and
perform a thorough investigation on bridging the gap in both the quantity and
quality of textual information between English and non-English languages. More
specifically, we: i) bring to light the problem of increasing multilingual
coverage 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, and
LLMs to generate high-quality textual information; and, iv) study the impact of
increasing multilingual coverage and precision of non-English textual
information in Entity Linking, Knowledge Graph Completion, and Question
Answering. As part of our effort towards better multilingual knowledge graphs,
we also introduce WikiKGE-10, the first human-curated benchmark to evaluate KGE
approaches in 10 languages across 7 language families.
