IEICE TRANSACTIONS on Information
Mining Approximate Primary Functional Dependency on Web Tables
Summary :
We propose to discover approximate primary functional dependency (aPFD) for web tables, which focus on the determination relationship between primary attributes and non-primary attributes and are more helpful for entity column detection and topic discovery on web tables. Based on association rules and information theory, we propose metrics Conf and InfoGain to evaluate PFDs. By quantifying PFDs' strength and designing pruning strategies to eliminate false positives, our method could select minimal non-trivial approximate PFD effectively and are scalable to large tables. The comprehensive experimental results on real web datasets show that our method significantly outperforms previous work in both effectiveness and efficiency.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.3 pp.650-654
- Publication Date
- 2019/03/01
- Publicized
- 2018/11/29
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8130
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Siyu CHEN
Beijing Jiaotong University
Ning WANG
Beijing Jiaotong University
Mengmeng ZHANG
North China University of Technology
Keyword
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Siyu CHEN, Ning WANG, Mengmeng ZHANG, "Mining Approximate Primary Functional Dependency on Web Tables" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 3, pp. 650-654, March 2019, doi: 10.1587/transinf.2018EDL8130.
Abstract: We propose to discover approximate primary functional dependency (aPFD) for web tables, which focus on the determination relationship between primary attributes and non-primary attributes and are more helpful for entity column detection and topic discovery on web tables. Based on association rules and information theory, we propose metrics Conf and InfoGain to evaluate PFDs. By quantifying PFDs' strength and designing pruning strategies to eliminate false positives, our method could select minimal non-trivial approximate PFD effectively and are scalable to large tables. The comprehensive experimental results on real web datasets show that our method significantly outperforms previous work in both effectiveness and efficiency.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8130/_p
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@ARTICLE{e102-d_3_650,
author={Siyu CHEN, Ning WANG, Mengmeng ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Mining Approximate Primary Functional Dependency on Web Tables},
year={2019},
volume={E102-D},
number={3},
pages={650-654},
abstract={We propose to discover approximate primary functional dependency (aPFD) for web tables, which focus on the determination relationship between primary attributes and non-primary attributes and are more helpful for entity column detection and topic discovery on web tables. Based on association rules and information theory, we propose metrics Conf and InfoGain to evaluate PFDs. By quantifying PFDs' strength and designing pruning strategies to eliminate false positives, our method could select minimal non-trivial approximate PFD effectively and are scalable to large tables. The comprehensive experimental results on real web datasets show that our method significantly outperforms previous work in both effectiveness and efficiency.},
keywords={},
doi={10.1587/transinf.2018EDL8130},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Mining Approximate Primary Functional Dependency on Web Tables
T2 - IEICE TRANSACTIONS on Information
SP - 650
EP - 654
AU - Siyu CHEN
AU - Ning WANG
AU - Mengmeng ZHANG
PY - 2019
DO - 10.1587/transinf.2018EDL8130
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2019
AB - We propose to discover approximate primary functional dependency (aPFD) for web tables, which focus on the determination relationship between primary attributes and non-primary attributes and are more helpful for entity column detection and topic discovery on web tables. Based on association rules and information theory, we propose metrics Conf and InfoGain to evaluate PFDs. By quantifying PFDs' strength and designing pruning strategies to eliminate false positives, our method could select minimal non-trivial approximate PFD effectively and are scalable to large tables. The comprehensive experimental results on real web datasets show that our method significantly outperforms previous work in both effectiveness and efficiency.
ER -
English
