Network Ranking Assisted Semantic Data Mining

  • Jan Kralj
  • Anže Vavpetič
  • Michel Dumontier
  • Nada Lavrač
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9656)


Semantic data mining (SDM) uses annotated data and interconnected background knowledge to generate rules that are easily interpreted by the end user. However, the complexity of SDM algorithms is high, resulting in long running times even when applied to relatively small data sets. On the other hand, network analysis algorithms are among the most scalable data mining algorithms. This paper proposes an effective SDM approach that combines semantic data mining and network analysis. The proposed approach uses network analysis to extract the most relevant part of the interconnected background knowledge, and then applies a semantic data mining algorithm on the pruned background knowledge. The application on acute lymphoblastic leukemia data set demonstrates that the approach is well motivated, is more efficient and results in rules that are comparable or better than the rules obtained by applying the incorporated SDM algorithm without network reduction in data preprocessing.


Background Knowledge Pattern Mining Inductive Logic Programming Subgroup Discovery PageRank Algorithm 
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 2016

Authors and Affiliations

  • Jan Kralj
    • 1
    • 2
  • Anže Vavpetič
    • 1
    • 2
  • Michel Dumontier
    • 4
  • Nada Lavrač
    • 1
    • 2
    • 3
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia
  2. 2.Jožef Stefan International Postgraduate SchoolLjubljanaSlovenia
  3. 3.University of Nova GoricaNova GoricaSlovenia
  4. 4.Stanford Center for Biomedical Informatics ResearchStanford UniversityStanfordUSA

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