Name
Title | ||
|---|---|---|
Development of NeuroElectroMagnetic ontologies(NEMO): a framework for mining brainwave ontologies |
Abstract | ||
|---|---|---|
Event-related potentials (ERP) are brain electrophysiological patterns created by averaging electroencephalographic (EEG) data, time-locking to events of interest (e.g., stimulus or response onset). In this paper, we propose a generic framework for mining anddeveloping domain ontologies and apply it to mine brainwave (ERP) ontologies. The concepts and relationships in ERP ontologies can be mined according to the following steps: pattern decomposition, extraction of summary metrics for concept candidates, hierarchical clustering of patterns for classes and class taxonomies, and clustering-based classification and association rules mining for relationships (axioms) of concepts. We have applied this process to several dense-array (128-channel) ERP datasets. Results suggest good correspondence between mined concepts and rules, on the one hand, and patterns and rules that were independently formulated by domain experts, on the other. Data mining results also suggest ways in which expert-defined rules might be refined to improve ontologyrepresentation and classification results. The next goal of our ERP ontology mining framework is to address some long-standing challenges in conducting large-scale comparison and integration of results across ERP paradigms and laboratories. In a more general context, this work illustrates the promise of an interdisciplinary research program, which combines data mining, neuroinformatics andontology engineering to address real-world problems. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1281192.1281224 | KDD |
Keywords | Field | DocType |
data mining,erp datasets,anddeveloping domain ontology,erp ontology,clustering-based classification,erp paradigm,mining brainwave ontology,association rules mining,erp ontology mining framework,neuroelectromagnetic ontology,classification result,data mining result,semantic web,association rule mining,hierarchical clustering | Ontology (information science),Hierarchical clustering,Research program,Ontology,Data mining,Neuroinformatics,Computer science,Axiom,Semantic Web,Association rule learning | Conference |
Citations | PageRank | References |
25 | 1.17 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dejing Dou | 1 | 892 | 90.86 |
| Gwen Frishkoff | 2 | 34 | 2.72 |
| Jiawei Rong | 3 | 51 | 5.54 |
| Robert Frank | 4 | 30 | 2.30 |
| Allen Malony | 5 | 94 | 8.29 |
| Don Tucker | 6 | 40 | 3.48 |