Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
SEGS: Search for enriched gene sets in microarray data
Journal of Biomedical Informatics
Feature Selection in Taxonomies with Applications to Paleontology
DS '08 Proceedings of the 11th International Conference on Discovery Science
Managing and Mining Graph Data
Managing and Mining Graph Data
Ideal downward refinement in the EL description logic
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Ontology-Enhanced association mining
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
Explaining subgroups through ontologies
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
ClowdFlows: a cloud based scientific workflow platform
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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With the expanding of the SemanticWeb and the availability of numerous ontologies which provide domain background knowledge and semantic descriptors to the data, the amount of semantic data is rapidly growing. The data mining community is faced with a paradigm shift: instead of mining the abundance of empirical data supported by the background knowledge, the new challenge is to mine the abundance of knowledge encoded in domain ontologies, constrained by the heuristics computed from the empirical data collection. We address this challenge by an approach, named semantic data mining, where domain ontologies define the hypothesis search space, and the data is used as means of constraining and guiding the process of hypothesis search and evaluation. The use of prototype semantic data mining systems SEGS and g-SEGS is demonstrated in a simple semantic data mining scenario and in two reallife functional genomics scenarios of mining biological ontologies with the support of experimental microarray data.