Increasing Coverage and Precision of Textual Information in Multilingual
  Knowledge Graphs
Simone Conia Min Li Daniel Lee Umar Farooq Minhas Ihab Ilyas Yunyao Li
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.