Name
Abstract | ||
|---|---|---|
In this paper, we propose an integrated OWL data mining and query system architecture for expressing the mined knowledge in the OWL format and for effectively answering users' queries. The proposed system consists of five sub-systems: query parser, rule inference system, ontology management system, knowledge generation system, and knowledge management system. We expect the architecture can provide users to query mined knowledge through the internet and with a more machine-understandable format. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICSMC.2008.4811283 | SMC |
Keywords | Field | DocType |
owl language,integrated owl data mining,query system architecture,mined knowledge,ontology management system,ontology,fquery parser,rule inference system,knowledge generation system,internet,owl format,ontologies (artificial intelligence),data mining,semantic web,association rules,web ontology language,machine-understandable format,query processing,knowledge management system,databases,management system,owl,system architecture,engines,association rule,ontologies | Query optimization,Ontology (information science),Web search query,Data mining,Query language,Information retrieval,Computer science,Semantic Web,Web query classification,Systems architecture,Database,Web Ontology Language | Conference |
ISSN | ISBN | Citations |
1062-922X E-ISBN : 978-1-4244-2384-2 | 978-1-4244-2384-2 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tzung-pei Hong | 1 | 3768 | 483.06 |
| Jun-Song Dong | 2 | 0 | 0.34 |
| Wen-Yang Lin | 3 | 399 | 35.72 |