Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24477-3_15 | Discovery Science |
Keywords | Field | DocType |
data mining community,domain ontology,simple semantic data mining,semantic data mining,semantic data,empirical data collection,semantic descriptors,experimental microarray data,empirical data,prototype semantic data mining,ontologies | Ontology (information science),Data science,Data mining,Concept mining,Data stream mining,Information retrieval,Biological Ontologies,Computer science,Relational data mining,Semantic analytics,Semantic computing,Semantic data model | Conference |
Volume | ISSN | Citations |
6926.0 | 0302-9743 | 9 |
PageRank | References | Authors |
0.70 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nada Lavrač | 1 | 989 | 72.19 |
| Anze Vavpetic | 2 | 52 | 6.49 |
| Larisa N. Soldatova | 3 | 180 | 20.75 |
| Igor Trajkovski | 4 | 54 | 5.84 |
| Petra Kralj Novak | 5 | 228 | 12.75 |