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.

自然语言处理和计算机视觉领域中近期的研究一直在利用知识图谱中可用的文本信息，例如实体名称和描述，来将神经模型与高质量的结构化数据对接。然而，在非英语语言中，可用的文本信息的数量和质量相对较少。为了解决这个问题，我们提出了自动知识图谱增强（KGE）的新任务，并对英语和非英语语言之间的文本信息的数量和质量差异进行了深入研究。作为对增加多语言覆盖率和实体名称、描述精确度的问题的研究，我们呈现了 M-NTA，一种新的无监督方法，结合了机器翻译（MT），网络搜索（WS）和大型语言模型（LLMs）以生成高质量的文本信息，并研究了增加非英语文本信息的多语言覆盖率和精确度对实体链接、知识图谱补全和问题回答的影响。作为我们朝着更好的多语种知识图谱的努力的一部分，我们还介绍了 WikiKGE-10，第一个跨 7 个语系评估 10 种语言中的 KGE 方法的人工策划的基准。

增加多语言知识图谱中文本信息的覆盖范围和精确度

Increasing Coverage and Precision of Textual Information in Multilingual  Knowledge Graphs

Recommender systems have found significant commercial success but still
struggle with integrating new users. Since users often interact with content in
different domains, it is possible to leverage a user's interactions in previous
domains to improve that user's recommendations in a new one (multi-domain
recommendation). A separate research thread on knowledge graph enhancement uses
external knowledge graphs to improve single domain recommendations (knowledge
graph enhancement). Both research threads incorporate related information to
improve predictions in a new domain. We propose in this work to unify these
approaches: Using information from interactions in other domains as well as
external knowledge graphs to make predictions in a new domain that would be
impossible with either information source alone. We apply these ideas to a
dataset derived from millions of users' requests for content across three
domains (videos, music, and books) in a live virtual assistant application. We
demonstrate the advantage of combining knowledge graph enhancement with
previous multi-domain recommendation techniques to provide better overall
recommendations as well as for better recommendations on new users of a domain.

本文提出了一种利用用户在其他领域的交互信息以及外部知识图谱的思想来改进新领域推荐的方法，并在虚拟助手应用程序中的三个领域（视频、音乐和图书）的大量用户请求的数据集上证明了该方法的优势。