Name
Abstract | ||
|---|---|---|
During the process of service retrieval, it's often difficult for users to give exact retrieval requirements because users are not familiar with the complex description mechanism of services. This limits the function of ontology service models and leads to lower completion, precision, efficiency and easiness of service retrieval. It is urgent to have an efficient method to help the users. The paper introduces a self-adaptive learning algorithm based on association mining theory in data mining field to learn from the retrieval history and assist users in giving high quality retrieval requirements. The experiment results show the effectivity of the proposed algorithm |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICICIC.2006.220 | ICICIC (2) |
Keywords | Field | DocType |
retrieval history,service retrieval assistance mechanism,information retrieval,high quality retrieval requirement,ontology,exact retrieval requirement,complex description mechanism,web service,data mining field,association mining,self-adaptive learning algorithm,internet,proposed algorithm,service retrieval,association mining theory,ontologies (artificial intelligence),data mining,ontol-ogy service model,be-cause user,unsupervised learning,adaptive learning,service model | Ontology,Cognitive models of information retrieval,Web mining,Human–computer information retrieval,Information retrieval,Data retrieval,Computer science,Unsupervised learning,Artificial intelligence,Web service,Machine learning,The Internet | Conference |
Volume | ISBN | Citations |
2 | 0-7695-2616-0 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Tang | 1 | 2 | 1.76 |
| Qian Leqiu | 2 | 27 | 6.12 |
| Yunjiao Xue | 3 | 63 | 9.07 |
| Hui Tang | 4 | 43 | 7.40 |