Community-Based Semantic Subgroup Discovery

  • Blaž Škrlj
  • Jan Kralj
  • Anže Vavpetič
  • Nada Lavrač
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10785)

Abstract

Modern data mining algorithms frequently need to address learning from heterogeneous data and knowledge sources, including ontologies. A data mining task in which ontologies are used as background knowledge is referred to as semantic data mining. A special form of semantic data mining is semantic subgroup discovery, where ontology terms are used in subgroup describing rules. We propose to enhance ontology-based subgroup identification by Community-Based Semantic Subgroup Discovery (CBSSD), taking into account also the structural properties of complex networks related to the studied phenomenon. The application of the developed CBSSD approach is demonstrated on two use cases from the field of molecular biology.

Keywords

Semantic data mining Bioinformatics Community detection Network analysis Term enrichment analysis 

Notes

Acknowledgments

This research was funded by the Slovenian Research Agency funded project HinLife: Analysis of Heterogeneous Information Networks for Knowledge Discovery in Life Sciences (J7-7303), as well as the The Human Brain Project (FET Flagship grant FP7-ICT-604102). The authors also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan-XP GPU used for this research.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Blaž Škrlj
    • 1
  • Jan Kralj
    • 2
  • Anže Vavpetič
    • 2
  • Nada Lavrač
    • 2
    • 3
  1. 1.Jožef Stefan International Postgraduate SchoolLjubljanaSlovenia
  2. 2.Jožef Stefan InstituteLjubljanaSlovenia
  3. 3.University of Nova GoricaNova GoricaSlovenia

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