Current clustering-based Open Relation Extraction (OpenRE) methods usually
adopt a two-stage pipeline. The first stage simultaneously learns relation
representations and assignments. The second stage manually labels several
instances and thus names the relation for each cluster. However, unsupervised
objectives struggle to optimize the model to derive accurate clustering
assignments, and the number of clusters has to be supplied in advance. In this
paper, we present a novel setting, named actively supervised clustering for
OpenRE. Our insight lies in that clustering learning and relation labeling can
be alternately performed, providing the necessary guidance for clustering
without a significant increase in human effort. The key to the setting is
selecting which instances to label. Instead of using classical active labeling
strategies designed for fixed known classes, we propose a new strategy, which
is applicable to dynamically discover clusters of unknown relations.
Experimental results show that our method is able to discover almost all
relational clusters in the data and improve the SOTA methods by 10.3\% and
5.2\%, on two datasets respectively.

本文提出一种名为主动监督聚类的新颖设置，该设置可以交替执行聚类学习和关系标签，为聚类提供必要的指导，而不需要显着增加人力劳动。

主动监督聚类用于开放式关系抽取

Actively Supervised Clustering for Open Relation Extraction

Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system
performance by providing structured information about entities and their
relationships, such as complementary or substitutable relations between
products or product types, which can be utilized in recommender systems.
However, relation labeling in KGs remains a challenging task due to the dynamic
nature of e-commerce domains and the associated cost of human labor. Recently,
breakthroughs in Large Language Models (LLMs) have shown surprising results in
numerous natural language processing tasks. In this paper, we conduct an
empirical study of LLMs for relation labeling in e-commerce KGs, investigating
their powerful learning capabilities in natural language and effectiveness in
predicting relations between product types with limited labeled data. We
evaluate various LLMs, including PaLM and GPT-3.5, on benchmark datasets,
demonstrating their ability to achieve competitive performance compared to
humans on relation labeling tasks using just 1 to 5 labeled examples per
relation. Additionally, we experiment with different prompt engineering
techniques to examine their impact on model performance. Our results show that
LLMs significantly outperform existing KG completion models in relation
labeling for e-commerce KGs and exhibit performance strong enough to replace
human labeling.

本文通过实证研究发现，大型语言模型在电商知识图谱中的关系标注任务中具有强大的学习能力和预测能力，相比于现有的知识图谱补全模型在关系标注任务上具有更高的竞争力，足以替代人工标注。