Title
Discovering Foreign Keys on Web Tables with the Crowd.
Abstract
Foreign-key relationship is one of the most important constraints between two tables. Previous works focused on detecting inclusion dependencies (INDs) or foreign keys in relational database. To discover foreign-key relationship is obviously helpful for analyzing and integrating data in web tables. However, because of poor quality of web tables, it is difficult to discover foreign keys by existing techniques based on checking basic integrity constraints. In this paper, we propose a hybrid human-machine framework to detect foreign keys on web tables. After discovering candidates and evaluating their confidence of being true foreign keys by machine algorithm, we verify those candidates leveraging the power of the crowd. To reduce the monetary cost, a dynamical task selection technique based on conflict detection and inclusion dependency is proposed, which could eliminate redundant tasks and assign the most valuable tasks to workers. Additionally, to make workers complete tasks more effectively and efficiently, sampling strategy is applied to mini-mize the number of tuples posed to the crowd. We conducted extensive experiments on real-world datasets and results show that our framework can obviously improve foreign key detection accuracy on web tables with lower monetary cost and time cost.
Year
DOI
Venue
2019
10.31577/cai_2019_3_621
COMPUTING AND INFORMATICS
Keywords
Field
DocType
Foreign key,web tables,crowdsourcing,task selection,task reduction,semantic recovery
World Wide Web,Computer science,Theoretical computer science,Foreign key,Web tables
Journal
Volume
Issue
ISSN
38
3
1335-9150
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Xiaoyu Wu101.01
Ning Wang238.48
Huaxi Liu311.02