Open-world Relation Extraction (OpenRE) has recently garnered significant
attention. However, existing approaches tend to oversimplify the problem by
assuming that all unlabeled texts belong to novel classes, thereby limiting the
practicality of these methods. We argue that the OpenRE setting should be more
aligned with the characteristics of real-world data. Specifically, we propose
two key improvements: (a) unlabeled data should encompass known and novel
classes, including hard-negative instances; and (b) the set of novel classes
should represent long-tail relation types. Furthermore, we observe that popular
relations such as titles and locations can often be implicitly inferred through
specific patterns, while long-tail relations tend to be explicitly expressed in
sentences. Motivated by these insights, we present a novel method called KNoRD
(Known and Novel Relation Discovery), which effectively classifies explicitly
and implicitly expressed relations from known and novel classes within
unlabeled data. Experimental evaluations on several Open-world RE benchmarks
demonstrate that KNoRD consistently outperforms other existing methods,
achieving significant performance gains.

这篇论文提出了一种名为 KNoRD 的方法，该方法在未标记数据中识别明确和隐含的关系，并有效地分类已知和新的关系类别，是 Open-world Relation Extraction 领域的最新研究成果。