Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The number of online software services based on SaaS paradigm is increasing. Hover, 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.  proposes a method for mining tags from the help documents of software services. By extracting terms from the help documents and calculating the similarity beten the terms,  construct a software similarity network where nodes represent software services, edges denote the similarity relationship beten software services, and the ights 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.
['Li Qin', 'Bing Li', 'Weifeng Pan', 'Tao Peng']
I am writing a research paper on a idea A Novel Method for Mining SaaS Software Tag via Community Detection in Software Services Network
[{"from": "human", "value": "I am writing a research paper on a idea A Novel Method for Mining SaaS Software Tag via Community Detection in Software Services Network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The number of online software services based on SaaS paradigm is increasing. Hover, 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.  proposes a method for mining tags from the help documents of software services. By extracting terms from the help documents and calculating the similarity beten the terms,  construct a software similarity network where nodes represent software services, edges denote the similarity relationship beten software services, and the ights 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."}]