Data In, Fact Out: Automated Monitoring of Facts by FactWatcher
Part of: 40th International Conference on Very Large Data Bases (VLDB 2014)
The Excellent Demonstration Award (3/42)
Authors
- Naeemul Hassan*
- Afroza Sultana*
- You Wu*
- Gensheng Zhang*
- Chengkai Li
- Jun Yang
- Cong Yu
*Authors are euqally contributed, and are ordered in alphabetical order.
Abstract
Towards computational journalism, we present FactWatcher, a system that helps journalists identify data-backed, attention-seizing facts which serve as leads to news stories. FactWatcher discovers three types of facts, including situational facts, one-of-the-few facts, and prominent streaks, through a unified suite of data model, algorithm framework, and fact ranking measure. Given an appendonly database, upon the arrival of a new tuple, FactWatcher monitors if the tuple triggers any new facts. Its algorithms efficiently search for facts without exhaustively testing all possible ones. Furthermore, FactWatcher provides multiple features in striving for an end-to-end system, including fact ranking, fact-to-statement translation and keyword-based fact search.
