Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/148886
Title: 
Authors: 
Borke, Lukas
Härdle, Wolfgang
Year of Publication: 
2016
Series/Report no.: 
SFB 649 Discussion Paper 2016-049
Abstract: 
QuantNet 1 is an integrated web-based environment consisting of different types of statistics-related documents and program codes. Its goal is creating reproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM) and latent semantic analysis (LSA). The LSA has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between documents. Our results show that different model configurations allow adapted similarity-based document clustering and knowledge discovery. In particular, different LSA configurations together with hierarchical clustering reveal good results under M3 evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under http://quantlet.de. The driving technology behind it is Q3-D3-LSA, which is the combination of 'GitHub API based QuantNet Mining infrastructure in R', LSA and D3 implementation.
Subjects: 
QuantNet
D3
GitHub API
text mining
document clustering
similarity
semantic web
generalized vector space model
LSA
visualization
Document Type: 
Working Paper

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