A New Measure of Edit Distance between Labeled Trees
COCOON '01 Proceedings of the 7th Annual International Conference on Computing and Combinatorics
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Correlating XML data streams using tree-edit distance embeddings
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Flexible Interface Matching for Web-Service Discovery
WISE '03 Proceedings of the Fourth International Conference on Web Information Systems Engineering
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Convolution Edit Kernel for Error-tolerant Graph Matching
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Web services discovery based on schema matching
ACSC '07 Proceedings of the thirtieth Australasian conference on Computer science - Volume 62
Similarity search for web services
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A generalization of Haussler's convolution kernel: mapping kernel
Proceedings of the 25th international conference on Machine learning
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With the development of e-commerce over Internet, web service discovery received much interest. A critical aspect of web service discovery is web service similarity search or matchmaking. To enhance the similarity precision, several solutions that do not limit to a syntactic comparison of inputs and outputs of the compared services have been proposed. Most of them introduce the structure of web service operations in the similarity measure. In this paper, we analyze these approaches and point out their time complexity drawback. Then, we propose a more efficient matching algorithm based on the concept of decomposition kernels of graphs. We study the complexity of our approach and present performance analysis.