Scalable spatio-temporal knowledge harvesting

Yafang Wang, Bin Yang, Spyros Zoupanos, Marc Spaniol, Gerhard Weikum

Abstract: Knowledge harvesting enables the automated construction of large knowledge bases. In this work, we made a first attempt to harvest spatio-temporal knowledge from news archives to construct trajectories of individual entities for spatio-temporal entity tracking. Our approach consists of an entity extraction and disambiguation module and a fact generation module which produce pertinent trajectory records from textual sources. The evaluation on the 20 years' New York Times news article corpus showed that our methods are effective and scalable.