Large databases of facts are prevalent in many applications. Such databases are accurate, but as they broaden their scope they become increasingly incomplete. In contrast to extending such a database, we present a system to query whether it contains an arbitrary fact. This work can be thought of as re-casting open domain information extraction: rather than growing a database of known facts, we smooth this data into a database in which any possible fact has membership with some confidence. We evaluate our system predicting held out facts, achieving 74.2% accuracy and outperforming multiple baselines. We also evaluate the system as a commonsense filter for the ReVerb Open IE system, and as a method for answer validation in a Question Answering task. 1 Introduction

Databases of facts, such as Freebase (Bollacker et al., 2008) or Open Information Extraction (Open IE) extractions, are useful for a range of NLP applications from semantic parsing to information extraction. However, as the domain of a database grows, it becomes increasingly impractical to collect completely, and increasingly unlikely that all the elements intended for the database are explicitly mentioned in the source corpus. In particular, common-sense facts are rarely explicitly mentioned, despite their abundance. It would be useful to infer the truth of such unseen facts rather than assuming them to be implicitly false. A growing body of work has focused on automatically extending large databases with a finite set of additional facts. In contrast, we propose a system to generate the (possibly infinite) completion of such a database, with a degree of confidence for each unseen fact. This task can be