Learning to Extract Events from Knowledge Base Revisions
Alexander Konovalov, Benjamin Strauss, Alan Ritter, Brendan O’Connor · 2017
Broad-coverage knowledge bases (KBs) such as Wikipedia, Freebase, Microsoft's Satori and Google's Knowledge Graph contain structured data describing real-world entities. These data sources have become increasingly important for a wide range of intelligent systems: from information retrieval and question answering, to Facebook's Graph Search, IBM's Watson, and more. Previous work on learning to populate knowledge bases from text has, for the most part, made the simplifying assumption that facts remain constant over time. But this is inaccurate -- we live in a rapidly changing world. Knowledge should not be viewed as a static snapshot, but instead a rapidly evolving set of facts that must change as the world changes.