Wikidata has grown to a knowledge graph with an impressive size. To date, it
contains more than 17 billion triples collecting information about people,
places, films, stars, publications, proteins, and many more. On the other side,
most of the information on the Web is not published in highly structured data
repositories like Wikidata, but rather as unstructured and semi-structured
content, more concretely in HTML pages containing text and tables. Finding,
monitoring, and organizing this data in a knowledge graph is requiring
considerable work from human editors. The volume and complexity of the data
make this task difficult and time-consuming. In this work, we present a
framework that is able to identify and extract new facts that are published
under multiple Web domains so that they can be proposed for validation by
Wikidata editors. The framework is relying on question-answering technologies.
We take inspiration from ideas that are used to extract facts from textual
collections and adapt them to extract facts from Web pages. For achieving this,
we demonstrate that language models can be adapted to extract facts not only
from textual collections but also from Web pages. By exploiting the information
already contained in Wikidata the proposed framework can be trained without the
need for any additional learning signals and can extract new facts for a wide
range of properties and domains. Following this path, Wikidata can be used as a
seed to extract facts on the Web. Our experiments show that we can achieve a
mean performance of 84.07 at F1-score. Moreover, our estimations show that we
can potentially extract millions of facts that can be proposed for human
validation. The goal is to help editors in their daily tasks and contribute to
the completion of the Wikidata knowledge graph.

基于问题回答技术，本研究提出了一个能从网页中提取新事实并推荐给 Wikidata 编辑人员验证的框架，并通过利用 Wikidata 中已有的信息，无需额外学习信号便可训练该框架来提取各种属性和领域的新事实，实验结果表明平均 F1 得分为 84.07，在人类验证之前，有潜力提取数百万条事实，旨在帮助编辑人员的日常任务，完善 Wikidata 知识图谱。

利用 Wikidata 作为 Web 抽取的种子

Wikidata as a seed for Web Extraction

As the field of Large Language Models (LLMs) evolves at an accelerated pace,
the critical need to assess and monitor their performance emerges. We introduce
a benchmarking framework focused on knowledge graph engineering (KGE)
accompanied by three challenges addressing syntax and error correction, facts
extraction and dataset generation. We show that while being a useful tool, LLMs
are yet unfit to assist in knowledge graph generation with zero-shot prompting.
Consequently, our LLM-KG-Bench framework provides automatic evaluation and
storage of LLM responses as well as statistical data and visualization tools to
support tracking of prompt engineering and model performance.

大型语言模型在知识图谱工程方面的性能评估和监控是一个重要问题，我们引入了一个基于知识图谱工程的基准评测框架，涵盖了语法和纠错、事实抽取以及数据集生成三个挑战。通过实验证明，尽管具有一定的实用性，大型语言模型在零样本生成知识图谱方面尚不适用。因此，我们的 LLM-KG-Bench 框架提供了 LLM 响应的自动评估和存储机制，以及统计数据和可视化工具，以支持指导数据和模型性能的追踪。