"A Spousal Relation Begins with a Deletion of engage and Ends with an Addition of divorce": Learning State Changing Verbs from Wikipedia Revision History

Learning to determine when the time-varying facts of a Knowledge Base (KB) have to be updated is a challenging task. We propose to learn state changing verbs from Wikipedia edit history. When a state-changing event, such as a marriage or death, happens to an entity, the in-fobox on the entity’s Wikipedia page usu-ally gets updated. At the same time, the article text may be updated with verbs ei-ther being added or deleted to reflect the changes made to the infobox. We use Wikipedia edit history to distantly super-vise a method for automatically learning verbs and state changes. Additionally, our method uses constraints to effectively map verbs to infobox changes. We observe in our experiments that when state-changing verbs are added or deleted from an en-tity’s Wikipedia page text, we can predict the entity’s infobox updates with 88 % pre-cision and 76 % recall. One compelling application of our verbs is to incorporate them as triggers in methods for updating existing KBs, which are currently mostly static.