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.
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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
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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
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