Partially squeezing the resources of the web of data towards applications

The Web of Data (WOD) contains a large amount of formalized and interconnected data, offering a valuable help for experimental tasks requiring an accurate data representation. However, the practical application of such data is often limited by the complexity when it comes to extracting the necessary information, mainly because of the lack of a proper structure and organization in the WOD-resources. The (re)organization of the knowledge contained in these resources might facilitate the identification of the necessary information and, consequently, limit the problems arising in their practical application. In this context, this paper proposes the application of Formal Concept Analysis (FCA) to create a concept-based abstraction that better organizes the knowledge contained in the WOD-resources. In order to test, to what extent this enhanced organization is able to improve the data representation process, the obtained FCA models will be tested in a practical application to represent a set of Twitter contents in a specific task: the Topic Detection task at Replab 2013. The results demonstrate that the better data representation obtained through FCA improves the operation of the topic detection process, outperforming state-of-the-art results.