Name
Abstract | ||
|---|---|---|
Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. Further, current clustering approaches are considered only have simple data types in services' input and output. However, services that published on the web have input/ output parameter of complex data type. In this research, we propose clustering approach that considers the simple type as well as complex data type in measuring the service similarity. We use hybrid term similarity method which we proposed in our previous work to measure the similarity. We capture the semantic pattern exist in complex data types and simple data types to improve the ontology learning method. Experimental results show our clustering approach which uses complex data types in measuring similarity works efficiently. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CIDM.2014.7008658 | Computational Intelligence and Data Mining |
Keywords | Field | DocType |
Web services,data mining,learning (artificial intelligence),ontologies (artificial intelligence),pattern clustering,Web service clustering,complex data type,hybrid term similarity method,ontology learning,semantic pattern,semantic similarity,service discovery,similarity-distance measurement method,Complex Data Type,Ontology Learning,Web Service Clustering | Semantic similarity,Ontology (information science),Data mining,Ontology-based data integration,Semantic Web Stack,Information retrieval,Computer science,OWL-S,Consensus clustering,Social Semantic Web,Cluster analysis | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Banage T. G. S. Kumara | 1 | 1 | 1.70 |
| Incheon Paik | 2 | 241 | 38.80 |
| Koswatte R. C. Koswatte | 3 | 16 | 3.11 |
| Wuhui Chen | 4 | 307 | 34.07 |