Open knowledge graph (KG) consists of (subject, relation, object) triples extracted from millions of raw text. The subject and object noun phrases and the relation in open KG have severe redundancy and ambiguity and need to be canonicalized. Existing datasets for open KG canonicalization only provide gold entity-level canonicalization for noun phrases. In this paper, we present COMBO, a Complete Benchmark for Open KG canonicalization. Compared with existing datasets, we additionally provide gold canonicalization for relation phrases, gold ontology-level canonicalization for noun phrases, as well as source sentences from which triples are extracted. We also propose metrics for evaluating each type of canonicalization. On the COMBO dataset, we empirically compare previously proposed canonicalization methods as well as a few simple baseline methods based on pretrained language models. We find that properly encoding the phrases in a triple using pretrained language models results in better relation canonicalization and ontology-level canonicalization of the noun phrase. We release our dataset, baselines, and evaluation scripts at https://github.com/jeffchy/COMBO/tree/main.

翻译：开放知识图( KG) 由( 主题、 关系、 对象) 3 组成的开放知识图( KG) 由( 主题、 关联、 对象) 数以百万计的原始文本中提取的三重( 主题、 关联、 对象名词 ) 组成。 主题和对象名词以及开放式 KG 中的关系有严重的冗余和模糊性, 需要加以解释。 开放的 KG Canicalization 的现有数据集只提供黄金实体级名词词的金色化。 在本文件中, 我们介绍 COMBO 完整基准, 开放 KG Canicalization 的完整基准 。 与现有数据集相比, 我们额外提供黄金的金色化, 相关词组的金色化, 名词组的金本级卡门级卡门化, 以及提取三重词的源句子句。 我们还提出评估每种类型的卡星化的参数 。 在COMBO/ ims/ bestal 上, 我们发布我们的数据基准/ base/ 。