Name
Abstract | ||
|---|---|---|
We hypothesize that the variance in volume of high-velocity queries over time can be explained by observing that these queries are formulated in response to events in the world that users are interested in. Based on it, this paper describes a system, Zed, which automatically finds explanations for high velocity queries, by extracting descriptions of relevant and temporally-proximate events from the news stream. Zed can thus provide a meaningful explanation of what the general public is interested in at any time. We evaluated performance of several variant methods on top velocity “celebrity name” queries from Yahoo, using news stories from several sources for event extraction. Results bear out the event-causation hypothesis, in that Zed currently finds acceptable event-based explanations for about 90% of the queries examined. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11811305_53 | ADMA |
Keywords | Field | DocType |
query volume,general public,top velocity,news stream,high-velocity query,celebrity name,high velocity query,event-causation hypothesis,acceptable event-based explanation,news story,event extraction,temporal variation | Data mining,Causality,Database query,Computer science,Temporal database,Reactive system | Conference |
Volume | ISSN | ISBN |
4093 | 0302-9743 | 3-540-37025-0 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maojin Jiang | 1 | 18 | 2.75 |
| Shlomo Argamon | 2 | 1171 | 102.93 |
| Abdur Chowdhury | 3 | 2013 | 160.59 |
| Kush Sidhu | 4 | 0 | 0.34 |