Web Service Clustering Approaches to Enhance Service Discovery: A Review

2021 ◽  
pp. 23-35
Author(s):  
Neha Agarwal ◽  
Geeta Sikka ◽  
Lalit Kumar Awasthi
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Service Clustering

Incorporating LDA With Word Embedding for Web Service Clustering

2018 ◽  
Vol 15 (4) ◽  
pp. 29-44 ◽  
Author(s):  
Yi Zhao ◽  
Chong Wang ◽  
Jian Wang ◽  
Keqing He
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Word Embedding ◽  
The Internet ◽  
Word Embeddings ◽  
Training Process ◽  
Web Service Discovery ◽  
Processing Data ◽  
Clustering Approach ◽  
Service Clustering

With the rapid growth of web services on the internet, web service discovery has become a hot topic in services computing. Faced with the heterogeneous and unstructured service descriptions, many service clustering approaches have been proposed to promote web service discovery, and many other approaches leveraged auxiliary features to enhance the classical LDA model to achieve better clustering performance. However, these extended LDA approaches still have limitations in processing data sparsity and noise words. This article proposes a novel web service clustering approach by incorporating LDA with word embedding, which leverages relevant words obtained based on word embedding to improve the performance of web service clustering. Especially, the semantically relevant words of service keywords by Word2vec were used to train the word embeddings and then incorporated into the LDA training process. Finally, experiments conducted on a real-world dataset published on ProgrammableWeb show that the authors' proposed approach can achieve better clustering performance than several classical approaches.

Context-Aware Web Service Clustering and Visualization

2020 ◽  
Vol 17 (4) ◽  
pp. 32-54
Author(s):  
Banage T. G. S. Kumara ◽  
Incheon Paik ◽  
Yuichi Yaguchi
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Search Space ◽  
Context Aware ◽  
Specific Context ◽  
Web Service Discovery ◽  
Domain Specific ◽  
Clustering Approach ◽  
Service Clustering

With the large number of web services now available via the internet, web service discovery has become a challenging and time-consuming task. Organizing web services into similar clusters is a very efficient approach to reducing the search space. A principal issue for clustering is computing the semantic similarity between services. Current approaches do not consider the domain-specific context in measuring similarity and this has affected their clustering performance. This paper proposes a context-aware similarity (CAS) method that learns domain context by machine learning to produce models of context for terms retrieved from the web. To analyze visually the effect of domain context on the clustering results, the clustering approach applies a spherical associated-keyword-space algorithm. The CAS method analyzes the hidden semantics of services within a particular domain, and the awareness of service context helps to find cluster tensors that characterize the cluster elements. Experimental results show that the clustering approach works efficiently.

A Semantic Approach of Service Clustering and Web Service Discovery

2013 ◽  
Vol 12 (5) ◽  
pp. 967-974 ◽  
Author(s):  
Yu Yue Du ◽  
Yong Jun Zhang ◽  
Xing Lin Zhang
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Semantic Approach ◽  
Web Service Discovery ◽  
Service Clustering

Web Service Clustering Using Relational Database Approach

2015 ◽  
Vol 25 (08) ◽  
pp. 1365-1393 ◽  
Author(s):  
Jianxiao Liu ◽  
Feng Liu ◽  
Xiaoxia Li ◽  
Keqing He ◽  
Yutao Ma ◽  
...  
Keyword(s):  
Web Services ◽  
Web Service ◽  
Relational Database ◽  
Service Discovery ◽  
The Self ◽  
Input Output ◽  
Computing Services ◽  
Service Information ◽  
Service Oriented ◽  
Service Clustering

In the era of service-oriented software engineering (SOSE), service clustering is used to organize Web services, and it can help to enhance the efficiency and accuracy of service discovery. In order to improve the efficiency and accuracy of service clustering, this paper uses the self-join operation in relational database (RDB) to realize Web service clustering. Based on storing service information, it does the self-join operation towards the Input, Output, Precondition, Effect (IOPE) tables of Web services, which can enhance the efficiency of computing services similarity. The semantic reasoning relationship between concepts and the concept status path are used to do the calculation, which can improve the calculation accuracy. Finally, we use experiments to validate the effectiveness of the proposed methods.

scholarly journals IMPROVING WEB SERVICE CLUSTERING THROUGH POST FILTERING TO BOOTSTRAP THE SERVICE DISCOVERY

2014 ◽  
Vol 2 (3) ◽  
pp. 1-13
Author(s):  
Banage T. G. S. Kumara ◽  
◽  
Incheon Paik ◽  
Koswatte R. C. Koswatte ◽  
Wuhui Chen ◽  
...  
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Service Clustering

Web Service Clustering and Data Mining in SOA System

2017 ◽  
pp. 157-177
Author(s):  
Sreeparna Mukherjee ◽  
Asoke Nath
Keyword(s):  
Data Mining ◽  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Software Components ◽  
Discovery Process ◽  
Data Mining Techniques ◽  
Web Service Discovery ◽  
Service Clustering ◽  
The Web

The success of the web depended on the fact that it was simple and ubiquitous. Over the years, the web has evolved to become not only the repository for accessing information but also for storing software components. This transformation resulted in increased business needs and with the availability of huge volumes of data and the continuous evolution in Web services functions derive the need of application of data mining in the Web service domain. Here we focus on applying various data mining techniques to the cluster web services to improve the Web service discovery process. We end this with the various challenges that are faced in this process of data mining of web services.

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