Link Prediction in Schema-Rich Heterogeneous Information Network

Cao, Xiaohuan1; Zheng, Yuyan1; Shi, Chuan1; Li, Jingzhi2; Wu, Bin1

10.1007/978-3-319-31753-3_36

2016

16113349

1611-3349

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2016, PT I

9651

449-460

Auckland, New zealand

HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY

SPRINGER-VERLAG BERLIN

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.

Heterogeneous Information Network Link prediction Similarity measure Meta path

其他

英语

CPCI-S ; EI

[2013CB329606] ; [JCYJ20140509143748226] ; National Natural Science Foundation of China[71231002] ; National Natural Science Foundation of China[61375058,11571161]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods

WOS:000389019500036

20161702311808

Data mining ; Forecasting ; Information services ; Knowledge based systems

Data Processing and Image Processing:723.2 ; Expert Systems:723.4.1 ; Information Services:903.4

Web of Science

1.Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China
2.Southern Univ Sci & Technol, Dept Math, Shenzhen 518055, Peoples R China

Cao, Xiaohuan,Zheng, Yuyan,Shi, Chuan,et al. Link Prediction in Schema-Rich Heterogeneous Information Network[C]. HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY:SPRINGER-VERLAG BERLIN,2016:449-460.