A systematic literature review on web service clustering approaches to enhance service discovery, selection and recommendation

WGSDMM+GA: A genetic algorithm-based service clustering methodology assimilating dirichlet multinomial mixture model with word embedding

The increasing number of Web services brings challenges for accurate and efficient service discovery. Service clustering technology can narrow the retrieval range of service discovery and improve the efficiency of service discovery. However, due to the short text features with few words and difficult feature extraction in service description text, the traditional text clustering model can not get effective clusters. In this paper, a web service clustering method based on word vector and BTM (Biterm Topic Model) is proposed. Firstly, the sparse feature problem of short text is alleviated by expanding the word vector. Secondly, the extended service description text is modeled by the BTM based on Gibbs sampling. Finally, the k-means algorithm is used to cluster web services. Compared with LDA (Latent Dirichlet Allocation) and BTM, this method improves the F-Measure of clustering index. Experimental results show that the proposed method can effectively alleviate the problem of sparse service description text features and improve the clustering effect of services.

Web Service Clustering Method Based on Word Vector and Biterm Topic Model

Due to the emergence of Internet technologies and service-oriented computing, there is a rapid growth in the quantity and variety of services on the web. Discovering the web services as per the request is not an easy task because of the advancement of service-oriented computing which includes web services, cloud services, mobile services, etc. These services are dynamic and published according to the emerging standards in repositories. Web service clustering plays a crucial role in web service discovery. When services are grouped according to the similarity, then it reduces the search space and time, so that services can be discovered efficiently. Many eminent researchers have proposed approaches for efficient web service discovery by incorporating web service clustering. In this paper, we review different approaches that are proposed for web service clustering to enhance the discovery process. A comparison among existing approaches is carried out based on the vector representation approach, dimensionality reduction technique, method to capture semantic relationship among features, clustering technique, type of input dataset, number of web services in dataset and service repository. This review will help the researchers to understand the existing techniques to group services in similar clusters to improvise service discovery and the scope for improvement by future directions.

Web Service Clustering Approaches to Enhance Service Discovery: A Review

In the current scenario, each task can be handled using Web APIs. Due to this, companies are providing services in the forms of Web APIs and Mashup APIs, which has generated a pool of services on the internet. It isn’t easy to search for the required services on the internet. It has resulted in an increase in the need for service management. If the services are properly grouped according to their actions and working, it will be easy to search for them. To group services, it is required that functionality of web APIs should be represented in vector space effectively. This service representation can be based on the frequency of features or statistical or semantic analysis of features. This article provides an overview of the many ways for representing services in vector space that are utilised in web API domains. This review paper will assist the researchers understand the existing techniques for service representation so that service management and grouping can be performed effectively.

Review of Feature Representation Techniques in Web APIs

Because of the short length of documents describing Web services on the Internet, the traditional modeling method is not ideal, which affects the clustering effect of Web services. To this end, we propose a word-embedded topic model, which can effectively solve the problem of data sparsity. A method based on embedded words and topic models. Firstly, Wikipedia is used as an external corpus to extend API service document, and LF-LDA model is used to model its topic distribution. The corpus data is extracted from Wikipedia by wikiextractor, and the corpus is trained with Word2vec tool. The data comes from its word vector model. Use the trained Wikipedia Word Vector Model to extend API service description documents. In the extended API service documents, LF-LDA topic modeling technology is used to mine the implicit topic information and determine the optimal number of topics, so as to accurately measure the semantic similarity between API service documents. Finally, K-means algorithm is used to cluster Web services, and the validity of this method is verified by the real data of API services on the basis of programmability. The experimental results show that this method has better accuracy, recall rate and F value than the traditional Web service clustering method based on topic model algorithm.

Research on Web Service Clustering Method Based on Word Embedding and Topic Model.

As the number and variety of services increase, it is becoming difficult and time-consuming to locate services that satisfy users' need. Service clustering is efficacious method to prune the query space, to narrow the searching space, and improve the accuracy of locating services that satisfied users' needs. At present, clustering method of web services adopted single or traditional clustering algorithms. However, accuracy and stability of single or traditional clustering algorithms is poor. In the paper, we proposed SWOC a service clustering method based on wisdom of crowd. Firstly, by using SWOC we calculated document similarity. Secondly, we implemented a mapping algorithm that reduces the correlation of web services and improve accuracy of method. And then, we applyed different number of clusters using different individual clustering methods that increase the number of partitions so as to enhance the robustness of SWOC. Lastly, the diversity algorithm evaluates and selects the partitions to extract interesting information for the final aggregation with the weight of each individual result. Experiments were performed on the real web service dataset crawled from ProgrammableWeb which prove the accuracy, recall, F-value and stability of proposed method.

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A Service Clustering Method Based on Wisdom of Crowds

The invention discloses a key service discovery method based on a service network. The key service discovery method based on the service network comprises the following steps: Step I, establishing theservice network and registering a service; Step II, when a new service is registered, triggering calculation of an importance degree of the new service and updating of a key service; and Step III, refreshing the importance degrees of entire network services and eliminating part of failed services. According to the key service discovery method disclosed by the invention, the key service is protected once the key service is discovered, and the service robustness is guaranteed; and the problem of a network service failure caused by simultaneous failures of a large number of compound services dueto failures of some services caused by certain factors is solved.

Key service discovery method based on service network

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Research on Web Service Clustering Method Based on Word Embedding and Topic Model.

A Service Clustering Method Based on Wisdom of Crowds

Key service discovery method based on service network