Learning hierarchical relationships among partially ordered objects with heterogeneous attributes and links

Chi Wang, Jiawei Han, Qi Li, Xiang Li, Wen Pin Lin, Heng Ji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Objects linking with many other objects in an information network may imply various semantic relationships. Uncovering such knowledge is essential for role discovery, data cleaning, and better organization of information networks, especially when the semantically meaningful relationships are hidden or mingled with noisy links and attributes. In this paper we study a generic form of relationship along which objects can form a treelike structure, a pervasive structure in various domains. We formalize the problem of uncovering hierarchical relationships in a supervised setting. In general, local features of object attributes, their interaction patterns, as well as rules and constraints for knowledge propagation can be used to infer such relationships. Existing approaches, designed for specific applications, either cannot handle dependency rules together with local features, or cannot leverage labeled data to differentiate their importance. In this study, we propose a discriminative undirected graphical model. It integrates a wide range of features and rules by defining potential functions with simple forms. These functions are also summarized and categorized. Our experiments on three quite different domains demonstrate how to apply the method to encode domain knowledge. The efficacy is measured with both traditional and our newly designed metrics in the evaluation of discovered tree structures.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
PublisherSociety for Industrial and Applied Mathematics Publications
Pages516-527
Number of pages12
ISBN (Print)9781611972320
DOIs
StatePublished - 2012
Event12th SIAM International Conference on Data Mining, SDM 2012 - Anaheim, CA, United States
Duration: Apr 26 2012Apr 28 2012

Publication series

NameProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Other

Other12th SIAM International Conference on Data Mining, SDM 2012
CountryUnited States
CityAnaheim, CA
Period4/26/124/28/12

ASJC Scopus subject areas

  • Computer Science Applications

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