Abstract

Learning to determine when the timevarying 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 infobox on the entity’s Wikipedia page usually gets updated. At the same time, the article text may be updated with verbs either being added or deleted to reflect the changes made to the infobox. We use Wikipedia edit history to distantly supervise 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 entity’s Wikipedia page text, we can predict the entity’s infobox updates with 88% precision 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.

Highlights

  • Extracting relational facts between entities and storing them in knowledge bases (KBs) has been a topic of active research in recent years

  • In this paper we presented a method that learns state changing verb phrases from Wikipedia revision history

  • We first constructed and curated a novel dataset from Wikipedia revision history that is tailored to our task

Read more

Summary

Introduction

Extracting relational facts between entities and storing them in knowledge bases (KBs) has been a topic of active research in recent years. We propose to predict state changes caused by verbs acting on entities in text. This is different from applying the same text extraction pipeline, that created the original KB, to dynamic Web content. Our assumption is that when a statechanging event happens to an entity e.g., a marriage, its Wikipedia infobox is updated by adding a new SPOUSE value. Wikipedia revision history of many articles can act as distant supervision data for learning the correspondence between text and infobox changes. (1) we present an algorithm that uses Wikipedia edit histories as distantly labeled data to learn which verbs result in which state changes to entities, and experimentally demonstrate its success, (2) we make available this set of distantly labeled training data on our website, and (3) we make available our learned mappings from verbs to state changes, as a resource for other researchers, on the same website

Data Construction
Feature Selection using Constraints
Experiments
Related Work
Findings
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.