A Service Discovery Method Based on Topic Filtering and Semantic Matching

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A Service Discovery Method Based on Topic Filtering and Semantic Matching

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[1]ZHOU Aohui,WENG Zhiyuan,ZHOU Siyuan,et al.A Service Discovery Method Based on Topic Filtering and Semantic Matching[J].Journal of Zhengzhou University (Engineering Science),2022,43(06):36-41.[doi:10.13705/j.issn.1671-6833.2022.06.003]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 06 Page number: 36-41 Column: Public date: 2022-09-02

Title:: A Service Discovery Method Based on Topic Filtering and Semantic Matching

Author(s):: ZHOU Aohui¹; WENG Zhiyuan²; ZHOU Siyuan¹; HUANG Qiao¹; WANG Ye¹; ZHANG Hua¹; 1.School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China; 、
2.Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln 68508, U.S.

Keywords:: service computing; business targets; service matching; recurrent neural networks; natural language processing

CLC:: TP311；O244

DOI:: 10.13705/j.issn.1671-6833.2022.06.003

Abstract:: Among the large number of available services on the internet, how to efficiently match the right service for a specific business target is a major challenge in current research. To address this problem, a method based on topic fitering and semantic matching was proposed that could be used for massive service discovery. The method first used Word2Vec to compare the similarity between the topic description text and the business target description text to obtain the business target topic, and then used TextRank to extract the service key sentences from the service description text. The service key sentences were filtered by the extracted business target topics to narrow the comparison range. Then, the word vector was extracted from the corresponding business goal and service description text, and the BiLSTM model with attention mechanism was used to calculate the similarity between them and return the list of the TOP-N services that were most similar to the business target description text for business developers for selection. And the data crawled from Programmable Web was annotated to build the business target-service sentence dataset required for the experiments, and evaluate the effectiveness of the methods in this study. Finally, the comparison results with models such as TextCNN , BiLSTM, and Word2VecSD showed that MAP of this method could be increase by 1.41 percentage points, 4.61 percentage points, and 4.95 percentage points. The finding of this study lay solid ground for further improvement in future work.

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Last Update: 2022-10-03