Scalable spatio-temporal knowledge harvesting

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 spatiotemporal 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. Categories and Subject Descriptors

• Type: Journal
• Year: 2011
• Where: WWW
• Authors: Yafang Wang, Bin Yang 0002, Spyros Zoupanos, Marc Spaniol, Gerhard Weikum
• Keywords: Internet Technology, Large Knowledge Bases, Pertinent Trajectory Records, Spatiotemporal Entity Tracking, WWW 2011