statistical topic models
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2016 ◽  
Vol 10 (04) ◽  
pp. 503-525
Author(s):  
Mehdi Allahyari ◽  
Krys Kochut

The volume of documents and online resources has been increasing significantly on the Web for many years. Effectively, organizing this huge amount of information has become a challenging problem. Tagging is a mechanism to aggregate information and a great step towards the Semantic Web vision. Tagging aims to organize, summarize, share and search the Web resources in an effective way. One important problem facing tagging systems is to automatically determine the most appropriate tags for Web documents. In this paper, we propose a probabilistic topic model that incorporates DBpedia knowledge into the topic model for tagging Web pages and online documents with topics discovered in them. Our method is based on integration of the DBpedia hierarchical category network with statistical topic models, where DBpedia categories are considered as topics. We have conducted extensive experiments on two different datasets to demonstrate the effectiveness of our method.


2016 ◽  
Vol 6 (2) ◽  
pp. 1-23
Author(s):  
Yi Yang ◽  
Shimei Pan ◽  
Jie Lu ◽  
Mercan Topkara ◽  
Yangqiu Song

2011 ◽  
Vol 88 (1-2) ◽  
pp. 157-208 ◽  
Author(s):  
Timothy N. Rubin ◽  
America Chambers ◽  
Padhraic Smyth ◽  
Mark Steyvers

Author(s):  
David Newman ◽  
Chaitanya Chemudugunta ◽  
Padhraic Smyth ◽  
Mark Steyvers

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