Name
Abstract | ||
|---|---|---|
Automatic information extraction techniques for knowledge acquisition are known to produce noise, incomplete or incorrect facts from textual sources. Human computing offers a natural alternative to expand and complement the output of automated information extraction methods, thereby enabling us to build high-quality knowledge bases. However, relying solely on human inputs for extraction can be prohibitively expensive in practice. We demonstrate human computing games for knowledge acquisition that employ human computing to overcome the limitations in automated fact acquisition methods. We provide a combined approach that tightly integrates automated extraction techniques with human computing for effective gathering of facts. The methods we provide gather facts in the form of relationships between entities. The games we demonstrate are specifically designed to capture hard-to-extract relations between entities in narrative text -- a task that automated systems find challenging. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2505515.2508213 | CIKM |
Keywords | Field | DocType |
high-quality knowledge base,automatic information extraction technique,automated system,automated extraction technique,human input,automated fact acquisition method,automated information extraction method,human computing,human computing game,knowledge acquisition,information extraction | Data mining,Computer science,Human computing,Narrative,Information extraction,Knowledge acquisition | Conference |
Citations | PageRank | References |
1 | 0.36 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarath Kumar Kondreddi | 1 | 11 | 1.68 |
| Peter Triantafillou | 2 | 1261 | 151.76 |
| Gerhard Weikum | 3 | 12710 | 2146.01 |