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Link Prediction in Schema-Rich Heterogeneous Information Network

  • Xiaohuan Cao
  • Yuyan Zheng
  • Chuan Shi
  • Jingzhi Li
  • Bin Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9651)

Abstract

Recent years have witnessed the boom of heterogeneous information network (HIN), which contains different types of nodes and relations. Many data mining tasks have been explored in this kind of network. Among them, link prediction is an important task to predict the potential links among nodes, which are required in many applications. The contemporary link prediction usually are based on simple HIN whose schema are bipartite or star-schema. In these HINs, the meta paths are predefined or can be enumerated. However, in many real networked data, it is hard to describe their network structure with simple schema. For example, the knowledge base with RDF format include tens of thousands types of objects and links. On this kind of schema-rich HIN, it is impossible to enumerate meta paths. In this paper, we study the link prediction in schema-rich HIN and propose a novel Link Prediction with automatic meta Paths method (LiPaP). The LiPaP designs an algorithm called Automatic Meta Path Generation (AMPG) to automatically extract meta paths from schema-rich HIN and a supervised method with likelihood function to learn weights of the extracted meta paths. Experiments on real knowledge database, Yago, validate that LiPaP is an effective, steady and efficient method.

Keywords

Heterogeneous Information Network Link prediction Similarity measure Meta path 

Notes

Acknowledgment

This work is supported in part by National Key Basic Research and Department (973) Program of China (No. 2013CB329606), and the National Natural Science Foundation of China (No. 71231002, 61375058,11571161), and the CCF-Tencent Open Fund, the Co-construction Project of Beijing Municipal Commission of Education, and Shenzhen Sci.-Tech Fund No. JCYJ20140509143748226.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiaohuan Cao
    • 1
  • Yuyan Zheng
    • 1
  • Chuan Shi
    • 1
  • Jingzhi Li
    • 2
  • Bin Wu
    • 1
  1. 1.Beijing Key Lab of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Department of MathematicsSouthern University of Science and TechnologyShenzhenChina

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