Paper Info
Title
Formal concept discovery in semantic web data
Abstract
Semantic Web efforts aim to bring the WWW to a state in which all its content can be interpreted by machines; the ultimate goal being a machine-processable Web of Knowledge. We strongly believe that adding a mechanism to extract and compute concepts from the Semantic Web will help to achieve this vision. However, there are a number of open questions that need to be answered first. In this paper we will establish partial answers to the following questions: 1) Is it feasible to obtain data from the Web (instantaneously) and compute formal concepts without a considerable overhead; 2) have data sets, found on the Web, distinct properties and, if so, how do these properties affect the performance of concept discovery algorithms; and 3) do state-of-the-art concept discovery algorithms scale wrt. the number of data objects found on the Web?
Year
DOI
Venue
2012
10.1007/978-3-642-29892-9_18
ICFCA
Keywords
Field
DocType
semantic web effort,distinct property,state-of-the-art concept discovery algorithm,formal concept,data object,concept discovery algorithm,semantic web data,formal concept discovery,considerable overhead,machine-processable web,semantic web,parallel algorithms,knowledge extraction,web of data
Web development,World Wide Web,Web intelligence,Semantic Web Stack,Computer science,Web standards,Semantic Web,Data Web,Web modeling,Social Semantic Web
Conference
Citations 
PageRank 
References 
14
0.75
14
Authors
6
Name
Order
Citations
PageRank
Markus Kirchberg149542.65
Erwin Leonardi216012.80
Yu Shyang Tan3704.58
Sebastian Link446239.59
Ryan K. L. Ko551531.73
Bu Sung Lee645235.22