Structured knowledge bases (KBs) are the backbone of many
know\-ledge-intensive applications, and their automated construction has
received considerable attention. In particular, open information extraction
(OpenIE) is often used to induce structure from a text. However, although it
allows high recall, the extracted knowledge tends to inherit noise from the
sources and the OpenIE algorithm. Besides, OpenIE tuples contain an open-ended,
non-canonicalized set of relations, making the extracted knowledge's downstream
exploitation harder. In this paper, we study the problem of mapping an open KB
into the fixed schema of an existing KB, specifically for the case of
commonsense knowledge. We propose approaching the problem by generative
translation, i.e., by training a language model to generate fixed-schema
assertions from open ones. Experiments show that this approach occupies a sweet
spot between traditional manual, rule-based, or classification-based
canonicalization and purely generative KB construction like COMET. Moreover, it
produces higher mapping accuracy than the former while avoiding the
association-based noise of the latter.

本文提出了一种基于生成式翻译的方法，通过训练语言模型，将开放式知识库映射到现有知识库的固定模式中，以此解决了开放式知识抽取中存在的问题。实验证明，该方法在传统方法和生成式知识库构建之间找到了一个甜点，并且在避免后者的相关性噪声的同时，比前者产生了更高的映射准确性。