Papers › Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs

Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs

27 Nov 2023arXiv:2311.15781archive 2025-07-28

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.

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normalize_value apple/ml-kge/src/evaluation/evaluate_coverage.py official repository ran fingerprinted Apache-2.0 (permissive) · 0501aeb16b4edb3c · report
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Tasks

Entity LinkingKnowledge Graph CompletionKnowledge GraphsMachine TranslationQuestion Answering

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