Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs
Simone Conia, Min Li, Daniel C. Lee, Umar Ibrahim Minhas, Ihab F. Ilyas, Yunyao Li · 2023
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.