Visualization of Semantic Data Based on Selected Predicates

Due to the spreading of semantic technologies, the volume of the datasets that are described in the Resource Description Framework (RDF) is dynamically growing. The RDF framework is suitable for integrating data from heterogeneous sources; however, the resulted datasets can be larger and extremely c...

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Bibliographic Details
Published inTransactions on Computational Collective Intelligence XIV Vol. 8615; pp. 180 - 195
Main Authors Rácz, Gábor, Gombos, Gergő, Kiss, Attila
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 01.01.2014
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Big data
Computer science
Hadoop
MapReduce
Semantic Web
Visualization
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Summary:Due to the spreading of semantic technologies, the volume of the datasets that are described in the Resource Description Framework (RDF) is dynamically growing. The RDF framework is suitable for integrating data from heterogeneous sources; however, the resulted datasets can be larger and extremely complex than before, new tools are needed to analyze them. In this paper, we present a method which aims to help to understand the structure of semantic datasets. It can reduce the size and the complexity of a dataset while preserves the selected parts of it. The method consists of a filtering and a compaction phases that are implemented according to the MapReduce distributed programing model to be able to handle large volume of data. The result of the method can be visualized as a labeled directed graph that is suitable to give an overview of the structure of the dataset. It may reveal hidden connections or different kinds of problems related to the completeness and correctness of the data.
ISBN:9783662445082
3662445085
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-662-44509-9_9