Name
Abstract | ||
|---|---|---|
The rapid growth of web services need efficiently discovering the desired web services for the users. Web service interfaces are defined with WSDL that is described by a bag of terms. Many similarity metrics are proposed to solve this problem, it is hardly to resolve the problem that only few pairs of terms between two services have high semantic distance, the semantic distance of other terms between two services are low. Using traditional keyword search metrics may acquire a wrong result that these two web services are similar. But semantics of the web services is hardly to exploit. In this work we use association rule to find terms that are often appear together and find the most similar terms. We weaken the weight of the most similar term contained in an association rule and enhance the other terms' weight contained in an association rule to solve the situation above. The experiments show that our approach outperforms some searching methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICCI-CC.2015.7259395 | 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) |
Keywords | Field | DocType |
similarity,information retrieval,association rule | Semantic similarity,Web intelligence,Information retrieval,Semantic Web Stack,Computer science,Web standards,Data Web,Social Semantic Web,Web service,Semantic Web Rule Language | Conference |
Citations | PageRank | References |
1 | 0.39 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Wang | 1 | 577 | 46.85 |
| Lingyu Xu | 2 | 3 | 5.50 |
| Jie Yu | 3 | 22 | 6.88 |
| Yunlan Xue | 4 | 7 | 5.09 |
| Gaowei Zhang | 5 | 7 | 5.09 |
| Xiangfeng Luo | 6 | 1251 | 124.38 |