A Web Service Clustering Method Based on Semantic Similarity and Multidimensional Scaling Analysis

Chuang Shan,Yugen Du,David Ruano-Ordás

doi:10.1155/2021/6661035

Chuang Shan, Yugen Du + Show 1 more

Open Access

https://doi.org/10.1155/2021/6661035

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Abstract

Clustering web services is an effective method to solving service computing problems. The key insight behind it is to extract the vectors based on the service description documents. However, the brevity of natural language service description documents typically complicates the vector construction process. To circumvent the difficulty, we propose a novel web service clustering method to vectorize documents based on the semantic similarity, which can be calculated via WordNet and multidimensional scaling (WMS) analysis. We utilize the dataset from the ProgrammableWeb to conduct extensive experiments and achieve prominent advances in precision, recall, and F-measure.

Highlights

Introduction rough the rapid development ofInternet technology [1], clustering web services has become an effective method to solving service discovery [2,3,4], service composition [5, 6], and service recommendation [7]
We improved the method of transposing data in multidimensional scaling analysis
We propose a web service clustering method based on semantic similarity and multidimensional scaling analysis

Summary

Introduction

Internet technology [1], clustering web services has become an effective method to solving service discovery [2,3,4], service composition [5, 6], and service recommendation [7]. It is intractable to search through the entire whole web services space and obtain accurate results. According to the work made by Zhang et al, clustering web services can improve the performance of service discovery by reducing the search space [2]. The scale of the repository will influence the efficiency of finding and sorting multiple web services with various functions. Clustering web services that matches the clusters to the requirements of developers can successfully alleviate this dilemma in the light of the research results of Xia et al [5]

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Results

Conclusion