Presented at: 9th Extended Semantic Web Conference (ESWC2012)
Statistics are very present in our daily lives. Every day, new statistics are published, showing the perceived quality of living in different cities, the corruption index of different countries, and so on. Interpreting those statistics, on the other hand, is a difficult task. Often, statistics collect only very few attributes, and it is difficult to come up with hypotheses that explain, e.g., why the perceived quality of living in once city is higher than in another. In this paper, we introduce Explain-a-LOD, an approach which uses data from Linked Open Data for generating hypotheses that explain statistics. We show an implemented prototype and compare different approaches for generating hypotheses by analyzing the perceived quality of those hypotheses in a user study.
Keywords: Applications, Linked Open Data, Rule Learning, Semantic Web, Statistics
Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2012/paper/in-use-industrial/9
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