Data Patterns Explained with Linked Data

  • Ilaria Tiddi
  • Mathieu d’Aquin
  • Enrico Motta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)


In this paper we present the system Dedalo, whose aim is to generate explanations for data patterns using background knowledge retrieved from Linked Data. In many real-world scenarios, patterns are generally manually interpreted by the experts that have to use their own background knowledge to explain and refine them, while their workload could be relieved by exploiting the open and machine-readable knowledge existing on the Web nowadays. In the light of this, we devised an automatic system that, given some patterns and some background knowledge extracted from Linked Data, reasons upon those and creates well-structured candidate explanations for their grouping. In our demo, we show how the system provides a step towards automatising the interpretation process in KDD, by presenting scenarios in different domains, data and patterns.


Background Knowledge Link Data Data Pattern Association Rule Mining Inductive Logic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK

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