Name
Abstract | ||
|---|---|---|
Hierarchies of concepts are useful in many applications from navigation to organization of objects. Usually, a hierarchy is created in a centralized manner by employing a group of domain experts, a time-consuming and expensive process. The experts often design one single hierarchy to best explain the semantic relationships among the concepts, and ignore the natural uncertainty that may exist in the process. In this paper, we propose a crowdsourcing system to build a hierarchy and furthermore capture the underlying uncertainty. Our system maintains a distribution over possible hierarchies and actively selects questions to ask using an information gain criterion. We evaluate our methodology on simulated data and on a set of real world application domains. Experimental results show that our system is robust to noise, efficient in picking questions, cost-effective, and builds high quality hierarchies. |
Year | Venue | Field |
|---|---|---|
2015 | IJCAI | Data science,Ask price,Crowdsourcing,Computer science,Information gain,Artificial intelligence,Hierarchy,Machine learning |
DocType | Volume | Citations |
Journal | abs/1504.07302 | 9 |
PageRank | References | Authors |
0.49 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuyin Sun | 1 | 205 | 8.42 |
| Adish Singla | 2 | 397 | 33.45 |
| Dieter Fox | 3 | 12306 | 1289.74 |
| Andreas Krause | 4 | 5822 | 368.37 |