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The Scientific World Journal
Volume 2014 (2014), Article ID 675234, 11 pages
Research Article

Constructing Topic Models of Internet of Things for Information Processing

1The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China
2Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China
3State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China

Received 25 May 2014; Accepted 18 June 2014; Published 9 July 2014

Academic Editor: Juncheng Jia

Copyright © 2014 Jie Xin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Internet of Things (IoT) is regarded as a remarkable development of the modern information technology. There is abundant digital products data on the IoT, linking with multiple types of objects/entities. Those associated entities carry rich information and usually in the form of query records. Therefore, constructing high quality topic hierarchies that can capture the term distribution of each product record enables us to better understand users’ search intent and benefits tasks such as taxonomy construction, recommendation systems, and other communications solutions for the future IoT. In this paper, we propose a novel record entity topic model (RETM) for IoT environment that is associated with a set of entities and records and a Gibbs sampling-based algorithm is proposed to learn the model. We conduct extensive experiments on real-world datasets and compare our approach with existing methods to demonstrate the advantage of our approach.