Learning Hierarchical Relationships among Partially Ordered Objects with Heterogeneous Attributes and Links

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, since it is 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 the structures. 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 discrimina...

• Type: Journal
• Year: 2012
• Where: SDM
• Authors: Chi Wang, Jiawei Han, Qi Li, Xiang Li, Wen-Pin Lin, Heng Ji
• Keywords: Data Mining, Discovery Data, Hierarchical Relationships, SDM 2012, Semantic Relationships