When peculiarity makes a difference: object characterisation in heterogeneous information networks

Chen, Wei, Zhu, Feida, Zhao, Lei and Zhou, Xiaofang (2016). When peculiarity makes a difference: object characterisation in heterogeneous information networks. In: Shamkant B. Navathe, Xiaoyong Du, Weili Wu, X. Sean Wang, Shashi Shekhar and Hui Xiong, Database Systems for Advanced Applications - 21st International Conference, DASFAA 2016, Proceedings, Part II. 21st International Conference on Database Systems for Advanced Applications, DASFAA 2016, Dallas TX, United States, (3-17). 16-29 April 2016. doi:10.1007/978-3-319-32049-6_1


Author Chen, Wei
Zhu, Feida
Zhao, Lei
Zhou, Xiaofang
Title of paper When peculiarity makes a difference: object characterisation in heterogeneous information networks
Conference name 21st International Conference on Database Systems for Advanced Applications, DASFAA 2016
Conference location Dallas TX, United States
Conference dates 16-29 April 2016
Proceedings title Database Systems for Advanced Applications - 21st International Conference, DASFAA 2016, Proceedings, Part II   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Cham, Switzerland
Publisher Springer International Publishing Switzerland
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1007/978-3-319-32049-6_1
Open Access Status Not Open Access
ISBN 9783319320489
9783319320496
ISSN 1611-3349
0302-9743
Editor Shamkant B. Navathe
Xiaoyong Du
Weili Wu
X. Sean Wang
Shashi Shekhar
Hui Xiong
Volume 9643
Start page 3
End page 17
Total pages 15
Chapter number 1
Total chapters 28
Collection year 2017
Language eng
Abstract/Summary A central task in heterogeneous information networks (HIN) is how to characterise an entity, which underlies a wide range of applications such as similarity search, entity profiling and linkage. Most existing work focus on using the main features common to all. While this approach makes sense in settings where commonality is of primary interest, there are many scenarios as important where uncommon and discriminative features are more useful. To address the problem, a novel model COHIN (Characterize Objects in Heterogeneous Information Networks) is proposed, where each object is characterized as a set of feature paths that contain both main and discriminative features. In addition, we develop an effective pruning strategy to achieve greater query performance. Extensive experiments on real datasets demonstrate that our proposed model can achieve high performance.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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