Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/77232
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Type: | Conference paper |
Title: | WS-finder: a framework for similarity search of web services |
Author: | Ma, J. Sheng, Q. Liao, K. Zhang, Y. Ngu, A. |
Citation: | Proceedings of the 10th International Conference on Service-Oriented Computing, held in Shanghai, China, 12-15 November, 2012 / C. Liu, H. Ludwig, F. Toumani and Q. Yu (eds.): pp.313-327 |
Publisher: | Springer-Verlag |
Publisher Place: | Germany |
Issue Date: | 2012 |
Series/Report no.: | Lecture Notes in Computer Science; 7636 |
ISBN: | 9783642343209 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | International Conference on Service-Oriented Computing (10th : 2012 : Shanghai, China) |
Statement of Responsibility: | Jiangang Ma, Quan Z. Sheng, Kewen Liao, Yanchun Zhang and Annew H.H. Ngu |
Abstract: | Most existing Web service search engines employ keyword search over databases, which computes the distance between the query and the Web services over a fixed set of features. Such an approach often results in incompleteness of search results. The Earth Mover’s Distance (EMD) has been successfully used in multimedia databases due to its ability to capture the differences between two distributions. However, calculating EMD is computationally intensive. In this paper, we present a novel framework calledWS-Finder, which improves the existing keywordbased search techniques for Web services. In particular, we employ EMD for many-to-many partial matching between the contents of the query and the service attributes. We also develop a generalized minimization lower bound as a new EMD filter for partial matching. This new EMD filter is then combined to a k-NN algorithm for producing complete top-k search results. Furthermore, we theoretically and empirically show that WS-Finder is able to produce query answers effectively and efficiently. |
Rights: | © Springer-Verlag Berlin Heidelberg 2012 |
DOI: | 10.1007/978-3-642-34321-6_21 |
Published version: | http://dx.doi.org/10.1007/978-3-642-34321-6_21 |
Appears in Collections: | Aurora harvest Computer Science publications |
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