Relation extraction (RE) is a well-known NLP application often treated as a
sentence- or document-level task. However, a handful of recent efforts explore
it across documents or in the cross-document setting (CrossDocRE). This is
distinct from the single document case because different documents often focus
on disparate themes, while text within a document tends to have a single goal.
Linking findings from disparate documents to identify new relationships is at
the core of the popular literature-based knowledge discovery paradigm in
biomedicine and other domains. Current CrossDocRE efforts do not consider
domain knowledge, which are often assumed to be known to the reader when
documents are authored. Here, we propose a novel approach, KXDocRE, that embed
domain knowledge of entities with input text for cross-document RE. Our
proposed framework has three main benefits over baselines: 1) it incorporates
domain knowledge of entities along with documents' text; 2) it offers
interpretability by producing explanatory text for predicted relations between
entities 3) it improves performance over the prior methods.

我们提出了一种新颖方法 KXDocRE，在跨文档关系抽取中嵌入了实体的领域知识，相比基准方法具有三个主要优势：1）它将实体的领域知识与文档的文本结合起来；2）它通过生成解释性文本来解释实体之间的预测关系；3）它在性能上优于先前的方法。