Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Service discovery method combining attention mechanism LSTM and neural topic model

A service discovery and topic model technology, applied in the computer field, can solve problems such as poor service discovery effect

Active Publication Date: 2020-10-20
WUHAN UNIV
View PDF24 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The present invention proposes a service discovery method based on an attention mechanism-based LSTM and a neural topic model, which is used to solve or at least partly solve the technical problem of poor service discovery effect existing in the methods in the prior art

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Service discovery method combining attention mechanism LSTM and neural topic model
  • Service discovery method combining attention mechanism LSTM and neural topic model
  • Service discovery method combining attention mechanism LSTM and neural topic model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0074] The present invention proposes a service discovery method combining attention mechanism LSTM and neural topic model. It can enhance the semantics of the original semantically sparse service description and user query, and then judge the matching degree of the service and user query based on the semantic similarity, and find services that can meet the functional needs of users in a large number of registered service libraries .

[0075] Technical scheme of the present invention is:

[0076] 1: Extract and preprocess the natural language vocabulary from the tags of the Web service description language; 2: Semantically enhance the keywords extracted in 1 and process the neural topic model to obtain the described topic information; 3: Utilize in The word vectors trained on the large-scale data set are used to embed the key words in 1; 4: On the basis of the completion of steps 2 and 3, the feature extraction of the description is performed through the bidirectional LSTM co...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a service discovery method combining an attention mechanism LSTM and a neural topic model. The service discovery method comprises the following steps: 1, extracting natural language vocabularies from a label of a Web service description language and preprocessing the natural language vocabularies; 2, conducting semantic enhancement on the keywords extracted in the step 1, and obtaining described topic information through processing by the neural topic model; 3, embedding the keywords in the step 1 by utilizing a pre-trained word vector; 4, on the basis of the step 2 andthe step 3, carrying out described feature extraction by combining bidirectional LSTM of an attention mechanism; and 5, on the basis of the step 4, finding k services with the highest similarity froma registration service library by calculating the similarity of the feature vectors of the query request and the service description. The method has the advantages that semantic information existingin description of Web services is processed and enhanced through external information, and services meeting user function requirements are found in a large number of registration services on the basisof semantics inquired by a user.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a service discovery method combining an attention mechanism-based LSTM and a neural topic model. Background technique [0002] Service-oriented architecture enables a new paradigm of software development and integration, where system functionality is encapsulated as loosely coupled and interoperable services. Therefore, in order to meet the high interoperability and flexibility requirements in the development of modern software applications, more and more web services and cloud services have been developed. The rapid increase in the number of Web services has brought convenience to developers, but at the same time it has brought difficulties to quickly select appropriate services to meet user needs from large-scale service registry. [0003] In existing service registries, most of Web services are described by (WSDL) Web service description language. The number of keywords ex...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F16/33G06F40/289G06F40/30G06N3/04G06N3/08
CPCG06F40/30G06F40/289G06F16/3344G06N3/049G06N3/08G06N3/044
Inventor 李兵姚力王健
Owner WUHAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com