In this work, we introduce and analyze an approach to knowledge transfer from
one collection of facts to another without the need for entity or relation
matching. The method works for both canonicalized knowledge bases and
uncanonicalized or open knowledge bases, i.e., knowledge bases where more than
one copy of a real-world entity or relation may exist. The main contribution is
a method that can make use of large-scale pre-training on facts, which were
collected from unstructured text, to improve predictions on structured data
from a specific domain. The introduced method is most impactful on small
datasets such as ReVerb20k, where a 6% absolute increase of mean reciprocal
rank and 65% relative decrease of mean rank over the previously best method was
achieved, despite not relying on large pre-trained models like Bert. To
understand the obtained pre-trained models better, we then introduce a novel
dataset for the analysis of pre-trained models for Open Knowledge Base
Completion, called Doge (Diagnostics of Open knowledge Graph Embeddings). It
consists of 6 subsets and is designed to measure multiple properties of a
pre-trained model: robustness against synonyms, ability to perform deductive
reasoning, presence of gender stereotypes, consistency with reverse relations,
and coverage of different areas of general knowledge. Using the introduced
dataset, we show that the existing OKBC models lack consistency in the presence
of synonyms and inverse relations and are unable to perform deductive
reasoning. Moreover, their predictions often align with gender stereotypes,
which persist even when presented with counterevidence. We additionally
investigate the role of pre-trained word embeddings and demonstrate that
avoiding biased word embeddings is not a sufficient measure to prevent biased
behavior of OKBC models.

介绍并分析一种无需实体或关系匹配将一个事实集合中的知识转移到另一个集合的方法，该方法对规范化知识库和非规范化或开放式知识库都适用。通过在无结构文本中收集的大规模预训练的事实，改进了对特定领域的结构化数据的预测，并通过实验验证了该方法在小数据集上的显著提升。此外，引入了一种新的用于分析开放式知识库完成的预训练模型的数据集，名为 Doge，通过该数据集揭示了现有模型的一些缺陷和问题。

知识库完成模型的预训练与诊断

Pre-training and Diagnosing Knowledge Base Completion Models

Automated completion of open knowledge bases (KBs), which are constructed
from triples of the form (subject phrase, relation phrase, object phrase)
obtained via open information extraction (IE) from text, is useful for
discovering novel facts that may not directly be present in the text. However,
research in open knowledge base completion (KBC) has so far been limited to
resource-rich languages like English. Using the latest advances in multilingual
open IE, we construct the first multilingual open KBC dataset, called mOKB6,
that contains facts from Wikipedia in six languages (including English).
Improving the previous open KB construction pipeline by doing multilingual
coreference resolution and keeping only entity-linked triples, we create a
dense open KB. We experiment with several baseline models that have been
proposed for both open and closed KBs and observe a consistent benefit of using
knowledge gained from other languages. The dataset and accompanying code will
be made publicly available.

利用跨语言的开放信息提取的最新进展，通过多语言代词消解和保留仅有实体链接三组，构建了第一个含有六种语言的全面的开放式知识库 (mOKB6)，并通过基线模型的实验进行分析。