Service discovery method based on clustering and Gaussian LDA

A service discovery and clustering technology, applied in the field of service computing, can solve the problems of large number of Web services, difficult management and retrieval, user semantic sparsity, etc., to achieve the effect of narrowing the search space, improving retrieval efficiency, and alleviating semantic sparsity

Pending Publication Date: 2020-10-27
CHONGQING UNIV
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[0004] In view of the above-mentioned problems existing in the prior art, the technical problems to be solved in the present invention are: the number

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  • Service discovery method based on clustering and Gaussian LDA
  • Service discovery method based on clustering and Gaussian LDA
  • Service discovery method based on clustering and Gaussian LDA

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Embodiment Construction

[0052] The present invention will be described in further detail below.

[0053] This paper proposes a Web service discovery method based on clustering and Gaussian LDA. The model as a whole is divided into three parts: service clustering, service modeling and service query.

[0054] Service clustering, including service clustering and cluster selection. For service clustering, use Doc2Vec to represent each Web service description document in the dataset as a fixed-dimensional vector, and then use the modified K-Means algorithm to cluster the Doc2Vec vector set. Cluster selection, after using the query expansion of the service query module to expand the user query, calculate the cosine similarity between the user query and each cluster for cluster selection.

[0055] Service modeling, using Word2Vec to represent all words in the dataset as a fixed-dimensional vector, and map the words to generate a corpus of target classes. Then, the two are used as the input of Gaussian LD...

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Abstract

The invention discloses a service discovery method based on clustering and Gaussian LDA, and the method comprises the following steps: carrying out the data analysis of a service data set, and carrying out the training of paragraph embedding and word embedding through Doc2Vec and Word2Vec; clustering the Doc2Vec vector set by using a modified K-Means algorithm; performing extended query based on the word embedding vector set to obtain an extended query statement Qe and an extended query vector Vqe; calculating the average cosine similarity between the expanded query statement and the Doc2Vec matrix of each clustering cluster obtained by clustering based on the expanded query statement, and taking the cluster with the highest similarity as a target cluster; constructing a Gaussian LDA modelbased on the selected target cluster and the word embedding vector obtained by training to obtain document-topic distribution and Gaussian distribution of topics; and calculating the probability thateach service in the target cluster is matched with the expanded user query by using the two distributions, and performing descending sort. The method is high in service matching accuracy.

Description

technical field [0001] The invention relates to the technical field of service computing, in particular to a service discovery method based on clustering and Gaussian LDA. Background technique [0002] Service discovery is one of the important components of service science. The development of enterprise service systems has changed dramatically with the growing popularity of service-oriented architectures. In addition, thanks to the rapid development of service-oriented computing, cloud computing technology and mobile Internet technology, the cost of service development, deployment, access, management and maintenance has dropped significantly. The combination of these two factors has led to the gradual increase in the popularity of services in the form of Web services, APIs (application programming interfaces), cloud services, and mashups, and massive services have emerged as the times require. Facing the explosive growth of Web services, how to efficiently and accurately m...

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Application Information

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IPC IPC(8): G06F40/216G06F40/289G06F40/30G06K9/62G06N3/04G06N3/08
CPCG06F40/216G06F40/289G06F40/30G06N3/08G06N3/045G06F18/22G06F18/23213G06F18/24Y02D10/00
Inventor 徐玲聂彤羽鄢萌王子梁张文燕付春雷张小洪
Owner CHONGQING UNIV
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