Building Top-k Consistent Results for Web Table Augmentation
Fei Qi, Xiaoyu Wu, Ning Wang · 2017
Web table augmentation enables users to augment attributes based on key column and other known information. For table augmentation, most of systems return a single result which could not meet the users' needs of selection and validation. Furthermore, previous works only consider the entity-attribute binary tables with the first column corresponding to the entity name and the second to an attribute to be extended. When a table has multiple columns to be extended, the result table consolidated by binary tables will suffer from entity inconsistency. In this paper, we present a framework called TAT to build Top-k consistent results for web table augmentation. While ensuring the consistency of entities, TAT provides as diverse results as possible. We design two algorithms, exclusive and iterative algorithm, for web table augmentation that return Top-k results based on different requirements from users. The experiments show that TAT could return Top-k consistent results without loss of precision or coverage.