2011
DOI: 10.1007/978-3-642-24477-3_15
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Abstract: Abstract.With the expanding of the Semantic Web 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… Show more

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Cited by 24 publications
(20 citation statements)
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“…The system was extended in the SegMine system, which allows exploratory analysis of microarray data, performed through semantic subgroup discovery by SEGS [177], followed by link discovery and visualization by Biomine [178], an integrated annotated bioinformatics information resource of interlinked data. The SEGS system was later extended to two general semantic subgroup discovery systems, SDM-SEGS and SDM-Aleph [179][180][181]. Finally, the authors introduced the Hedwig system [182], which overcomes some of the limitations of the previous systems.…”
Section: Discussionmentioning
confidence: 99%
“…The system was extended in the SegMine system, which allows exploratory analysis of microarray data, performed through semantic subgroup discovery by SEGS [177], followed by link discovery and visualization by Biomine [178], an integrated annotated bioinformatics information resource of interlinked data. The SEGS system was later extended to two general semantic subgroup discovery systems, SDM-SEGS and SDM-Aleph [179][180][181]. Finally, the authors introduced the Hedwig system [182], which overcomes some of the limitations of the previous systems.…”
Section: Discussionmentioning
confidence: 99%
“…semantic descriptors to the data and background knowledge (Lavrač et al 2011;Pinto, Santos 2009;Arantes 2011). Therefore, the domain ontology is helpful for data analysis and finding different data patterns.…”
Section: Ontologiesmentioning
confidence: 99%
“…Standard data mining algorithms usually use statistical models on data to discover patterns and provide actionable insights. According to Lavrač et al (2011), in these cases, data is treated as meaningless numbers and attribute values. In other words, data by itself does not convey any semantics and needs to be interpreted to present meaningful information, which is usually done by domain experts.…”
Section: Semantic Data Miningmentioning
confidence: 99%