Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2016

Abstract

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.

Keywords

Database systems, Query processing, Discriminative features, Heterogeneous information, Pruning strategy, Query performance, Real data sets, Similarity search, Engineering main heading: Information services

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Database Systems for Advanced Applications: 21st International Conference, DASFAA 2016, Dallas, TX, April 16-19: Proceedings

Volume

9643

First Page

3

Last Page

17

ISBN

9783319320489

Identifier

10.1007/978-3-319-32049-6_1

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-319-32049-6_1

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