  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.

Computation and Language