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.
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Notes
- 1.
Plotted with the Py3Plex library (https://github.com/SkBlaz/Py3Plex).
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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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Škrlj, B., Kralj, J., Vavpetič, A., Lavrač, N. (2018). Community-Based Semantic Subgroup Discovery. In: Appice, A., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2017. Lecture Notes in Computer Science(), vol 10785. Springer, Cham. https://doi.org/10.1007/978-3-319-78680-3_13
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