2016/12/30 by Massimiliano Dal Mas, Mas, Massimiliano Dal · 1 citation
Computer Science · #03B65 #03G10 #68M11 #68P05 #68Q55 #68T30 #68U35 #Computation and Language (cs.CL) #Computers and Society (cs.CY) #D.2.2 #FOS: Computer and information sciences #G.1.10 #G.2.2 #H.1.1 #H.1.2 #H.3.1 #H.3.3 #H.3.5 #H.5.2 #H.5.3 #H.5.4 #I.2.1 #I.2.4 #I.2.7 #I.3.6 #Information Retrieval (cs.IR) #K.4 #Social and Information Networks (cs.SI) #acm:03B65 #acm:03G10 #acm:68M11 #acm:68P05 #acm:68Q55 #acm:68T30 #acm:68U35 #cs.CL #cs.CY #cs.IR #cs.SI #msc:03B65 #msc:03G10 #msc:68M11 #msc:68P05 #msc:68Q55 #msc:68T30 #msc:68U35
paper · pdf · doi:10.48550/arxiv.1612.09574
8 pages, 3 figures; 2 tables; for details see: http://www.maxdalmas.com
arxiv created 2016/12/30 · arxiv updated 2017/01/02
The Folksodriven framework makes it possible for data scientists to define an ontology environment where searching for buried patterns that have some kind of predictive power to build predictive models more effectively. It accomplishes this through an abstractions that isolate parameters of the predictive modeling process searching for patterns and designing the feature set, too. To reflect the evolving knowledge, this paper considers ontologies based on folksonomies according to a new concept structure called "Folksodriven" to represent folksonomies. So, the studies on the transformational regulation of the Folksodriven tags are regarded to be important for adaptive folksonomies classifications in an evolving environment used by Intelligent Systems to represent the knowledge sharing. Folksodriven tags are used to categorize salient data points so they can be fed to a machine-learning system and "featurizing" the data.