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Web service discovery method based on label sparse learning

A discovery method and web service technology, applied in special data processing applications, instruments, network data retrieval, etc., can solve problems such as inefficient labeling, scarcity of labels, random non-standardization, etc., and achieve the effect of improving efficiency and accuracy

Active Publication Date: 2015-12-09
ZHEJIANG UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

Labels mainly rely on manual labeling. Compared with the growth of big data services, such labeling is too inefficient, resulting in labels that are always scarce
[0006] 2. Since tags are manually marked, there are shortcomings such as arbitrariness and non-standardization, simply using query requests and tags to match will directly reduce the service discovery effect

Method used

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  • Web service discovery method based on label sparse learning
  • Web service discovery method based on label sparse learning
  • Web service discovery method based on label sparse learning

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Embodiment Construction

[0026] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0027] Such as figure 1 As shown, the Web service discovery method based on label sparse learning of the present invention comprises the following parts:

[0028] Step 1: The service search engine collects the WSDL files provided by the service developers. For each service file, the engine manages user provided tag information. Assuming that the developer provides a total of D services as search engine candidate sets, then there are a total of D WSDL files describing the corresponding services. In the initialization stage, the user tags D service files to explain the function of the service, and the quality of the tags is guaranteed by the service search engine mechanism during the process. After collection, a "one-to-many" mapping relationship is esta...

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Abstract

The present invention discloses a Web service discovery method based on label sparse learning. The goal of Web service discovery method based on label sparse learning is to break through a status that a single current service data source is used, making full use of the discovery process of a text information optimization service. The method comprises: first extracting a service description file and associated label text information by using an open source tool; then mining a hidden relationship between the service description file and label by using a sparse model tool, and finally achieving a precise label prediction function by optimizing learning. According to the method, the text features of WSDL are sufficiently mined to improve the accuracy of label prediction; furthermore, the Web service discovery method based on label sparse learning can response personalized query requests of multiuser in real time by using a two-stage hybrid intelligent algorithm, and the produced label prediction list is beneficial to improving the effectiveness of Web service discovery.

Description

technical field [0001] The invention belongs to the technical field of computer services, and in particular relates to a method for discovering web services based on label sparse learning. Background technique [0002] With the continuous development of the scientific and technological revolution in the Web2.0 era, the main form, operation mode, production mode and usage mode of Internet software production methods are undergoing tremendous changes. Distributed service discovery based on Web service dynamic aggregation, automatic composition and elastic scaling has become an important trend in future network application development. These Web service technology applications are all developed on the basis of service search engine discovery and management services. In recent years, the use of search engines to discover services has become the focus of industry and academia. [0003] At present, web services are mainly aggregated and managed through search engines. In actual...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/90344G06F16/951G06F16/9535
Inventor 尹建伟罗威邓水光李莹吴健吴朝晖
Owner ZHEJIANG UNIV
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