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10.1371/journal.pone.0176310 http://scihub22266oqcxt.onion/10.1371/journal.pone.0176310 C5405987!5405987!28445550     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28445550&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       PLoS+One 2017 ; 12 (4): ä Nephropedia Template TP {{tp|p=28445550|t=2017. Classifying patents based on their semantic content |pdf=|usr=}}{{28445550}} gab.com Text Classifying patents based on their semantic content JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28445550 #FOAvip In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information. Twit Text FOAVip Classifying patents based on their semantic content ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28445550 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28445550 #FOAvip English Wikipedia <ref>{{Cite pmid|28445550}}</ref> Classifying patents based on their semantic content #MMPMID28445550 Bergeaud A; Potiron Y; Raimbault J PLoS One 2017[]; 12 (4): ä PMID28445550show ga In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information. äClassifying patents based on their semantic content (epmc).pdf DeepDyve Pubget Overpricing