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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Formal Concept Discovery in Semantic Web Data
verfasst von : Markus Kirchberg, Erwin Leonardi, Yu Shyang Tan, Sebastian Link, Ryan K. L. Ko, Bu Sung Lee
Erschienen in: Formal Concept Analysis
Verlag: Springer Berlin Heidelberg
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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?