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.

翻译：结构化知识库是众多知识密集型应用的支柱，其自动化构建已受到广泛关注。其中，开放信息抽取（OpenIE）常被用于从文本中提取结构信息。然而，尽管该方法能实现高召回率，但提取的知识往往继承自来源和OpenIE算法的噪声。此外，OpenIE元组包含开放式的、非规范化的关系集合，这增加了下游对提取知识的利用难度。本文研究了将开放知识库映射至现有固定模式知识库的问题，特别针对常识知识场景。我们提出通过生成式翻译方法解决该问题，即训练语言模型从开放断语生成符合固定模式的断语。实验表明，该方法在传统的手工、基于规则或基于分类的规范化方法与纯生成式知识库构建方法（如COMET）之间取得了平衡。相较于前者，它实现了更高的映射准确率，同时避免了后者的关联噪声。