Relation extraction is an efficient way of mining the extraordinary wealth of
human knowledge on the Web. Existing methods rely on domain-specific training
data or produce noisy outputs. We focus here on extracting targeted relations
from semi-structured web pages given only a short description of the relation.
We present GraphScholarBERT, an open-domain information extraction method based
on a joint graph and language model structure. GraphScholarBERT can generalize
to previously unseen domains without additional data or training and produces
only clean extraction results matched to the search keyword. Experiments show
that GraphScholarBERT can improve extraction F1 scores by as much as 34.8\%
compared to previous work in a zero-shot domain and zero-shot website setting.

关系抽取是一种从网络上挖掘人类知识的高效方式，本文提出了一种名为 GraphScholarBERT 的开放领域信息抽取方法，它能够从半结构化的网页中提取目标关系，并能在未见过的领域中进行泛化，无需额外数据或训练，并且与搜索关键词完全匹配。实验证明，GraphScholarBERT 在零射击域和零射击网站情境下，相比以往的方法，可以提高抽取的 F1 分数达 34.8%。