Abstract
The number of online software services based on SaaS paradigm is increasing. However, users usually find it hard to get the exact software services they need. At present, tags are widely used to annotate specific software services and also to facilitate the searching of them. Currently these tags are arbitrary and ambiguous since mostly of them are generated manually by service developers. This paper proposes a method for mining tags from the help documents of software services. By extracting terms from the help documents and calculating the similarity between the terms, we construct a software similarity network where nodes represent software services, edges denote the similarity relationship between software services, and the weights of the edges are the similarity degrees. The hierarchical clustering algorithm is used for community detection in this software similarity network. At the final stage, tags are mined for each of the communities and stored as ontology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cloud Computing From Wikipedia, http://en.wikipedia.org/wiki/Cloud_computing
Google and IBM Join in Cloud Computing Research (2007), http://www.nytimes.com/
How Are SaaS and Cloud Computing Related? (2009), http://caas.tmcnet.com/
Two hot technologies: Saas and cloud computing (2008), http://dev.yesky.com/
What is cloud computing means? (2008), http://news.csdn.net/
ICTCLAS3.0, Website, http://ictclas.org/
Nakagawa, H., Mori, T.: A simple but powerful automatic term extraction method. In: COMPUTERM 2002, pp. 1–7 (2002)
Jiang, X., Tan, A.-H.: Mining ontological knowledge from domain-specific text documents. In: Fifth IEEE ICDM, pp. 27–30 (2005)
Song, N.-R., Feng, Z.-W., Kit, C.-Y.: Automatic Chinese Multi-word Term Extraction. In: ALPIT 2008, pp. 181–184. IEEE Press, Dalian (2008)
Li, W., Wang, C., Shi, D.-n.: Automatic Chinese Term Extraction based on Cognition Theory. In: ICNSC 2008, pp. 170–174 (2008)
Hong-Minh, T., Smith, D.: Word Similarity In WordNet.: Modeling, Simulation and Optimization of Complex Processes. In: Proceedings of the Third International Conference on High Performance Scientific Computing, 2006, Hanoi, Vietnam, pp. 293–302. Springer, Heidelberg (2008)
Liu, Q., Li, S.-J.: A word similarity computing method based on HowNet. In: 3th Chinese Lexical Semantics Workshop, Taipei (2002)
Dong, Z.-D., Dong, Q.: HowNet Website, http://www.keenage.com/
Salton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1988)
Peng, J., Yang, D.-Q., Tang, S.-W.: A Novel Text Clustering Algorithm Based on Inner Product Space Model of Semantic. Chinese Journal of Computers 30(8), 1354–1363 (2007)
Pan, W.-f., Li, B., Ma, Y.-t., Liu, J., Qin, Y.-y.: Class structure refactoring of object-oriented softwares using community detection in dependency networks. Frontiers of Computer Science in China 3(3), 396–404 (2009)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Rev. E 69, 066133 (2004)
alisoft.com, http://mall.alisoft.com/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qin, L., Li, B., Pan, WF., Peng, T. (2009). A Novel Method for Mining SaaS Software Tag via Community Detection in Software Services Network. In: Jaatun, M.G., Zhao, G., Rong, C. (eds) Cloud Computing. CloudCom 2009. Lecture Notes in Computer Science, vol 5931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10665-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-10665-1_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10664-4
Online ISBN: 978-3-642-10665-1
eBook Packages: Computer ScienceComputer Science (R0)