YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Timely YAGO: harvesting, querying, and visualizing temporal knowledge from Wikipedia
Proceedings of the 13th International Conference on Extending Database Technology
Event-centric search and exploration in document collections
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
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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.