Web Service Clustering Approach Based on Network and Fused Document-Based and Tag-Based Topics Similarity

2021 ◽  
Vol 18 (3) ◽  
pp. 63-81
Author(s):  
Deng Li Ping ◽  
Guo Bing ◽  
Zheng Wen
Keyword(s):  
Web Services ◽  
Web Service ◽  
Auxiliary Information ◽  
Service Network ◽  
New Approach ◽  
Data Network ◽  
Modal Data ◽  
Clustering Problem ◽  
Clustering Approach ◽  
Service Clustering

To produce a web services clustering with values that satisfy many requirements is a challenging focus. In this article, the authors proposed a new approach with two models, which are helpful to the service clustering problem. Firstly, a document-tag LDA model (DTag-LDA) is proposed that considers the tag information of web services, and the tag can describe the effective information of documents accurately. Based on the first model, this article further proposes an efficient document weight and tag weight-LDA model (DTw-LDA), which fused multi-modal data network. To further improve the clustering accuracy, the model constructs the network for describing text and tag respectively and then merges the two networks to generate web service network clustered. In addition, this article also designs experiments to verify that the used auxiliary information can help to extract more accurate semantics by conducting service classification. And the proposed method has obvious advantages in precision, recall, purity, and other performance.

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.

Multi-Level Web Service Clustering to Bootstrap the Web Service Discovery and Selection

2019 ◽  
pp. 44-68
Author(s):  
Banage T. G. S. Kumara ◽  
Incheon Paik ◽  
Koswatte R. C. Koswatte
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Service Discovery ◽  
Value Added ◽  
Search Space ◽  
Clustering Approach ◽  
Multi Level ◽  
Service Clustering ◽  
Service Discovery And Selection

Existing technologies for web services have been extended to give the value-added customized services to users through the service composition. Service composition consists of four major stages: planning, discovery, selection, and execution. However, with the proliferation of web services, service discovery and selection are becoming challenging and time-consuming tasks. Organizing services into similar clusters is a very efficient approach. Existing clustering approaches have problems that include discovering semantic characteristics, loss of semantic information, and a shortage of high-quality ontologies. Thus, the authors proposed hybrid term similarity-based clustering approach in their previous work. Further, the current clustering approaches do not consider the sub-clusters within a cluster. In this chapter, the authors propose a multi-level clustering approach to prune the search space further in discovery process. Empirical study of the prototyping system has proved the effectiveness of the proposed multi-level clustering approach.

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.

Result Refinement in Web Services Retrieval Based on Multiple Instances Learning

2011 ◽  
pp. 340-357 ◽  
Author(s):  
Yanzhen Zou ◽  
Lu Zhang ◽  
Yan Li ◽  
Bing Xie ◽  
Hong Mei
Keyword(s):  
Web Services ◽  
Web Service ◽  
Experimental Results ◽  
Retrieval Process ◽  
New Approach ◽  
Critical Step ◽  
Uddi Registry ◽  
Service Reuse

Web services retrieval is a critical step for reusing existing services in the SOA paradigm. In the UDDI registry, traditional category-based approaches have been used to locate candidate services. However, these approaches usually achieve relatively low precision because some candidate Web Services in the result set cannot provide actually suitable operations for users. In this article, we present a new approach to improve this kind of category-based Web Services retrieval process that can refine the coarse matching results step by step. The refinement is based on the idea that operation specification is very important to service reuse. Therefore, a Web Service is investigated via multiple instances view in our approach, which indicates that a service is labeled as positive if and only if at least one operation provided by this service is usable to the user. Otherwise, it is labeled as negative. Experimental results demonstrate that our approach can increase the retrieval precision to a certain extent after one or two rounds of refinement.

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.

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.

An Evolving Web Service Interaction Network Model

2014 ◽  
Vol 610 ◽  
pp. 559-567
Author(s):  
Yong Kui Liu ◽  
Lin Zhang ◽  
Fei Tao ◽  
Long Wang
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Empirical Studies ◽  
Interaction Network ◽  
Interaction Networks ◽  
The Internet ◽  
Service Networks ◽  
Service Network ◽  
Service Interaction

With the rapid growth of Web services on the Internet, the atomic Web services as nodes and their functionality dependency relationships as edges form a complex Web service network. Various interactions between Web services can occur along the edges, such as collaboration, competition and substitution, etc. So far, however, there lack of an effective and scalable model for generating Web service interaction networks capturing the aforementioned types of interactions, which hinders the relevant researches such as development of new service composition algorithms and the investigation of evolution mechanisms of service networks. In this paper, we propose a model which is able to generate two types of Web service interaction networks, namely complementary Web service interaction network (CWSIN) and similar Web service interaction network (SWSIN). We show that CWSIN exhibits some of the typical characteristics reported in the previous empirical studies.

Information Retrieval from Deep Web Based on Visual Query Interpretation

2012 ◽  
Vol 2 (4) ◽  
pp. 45-59 ◽  
Author(s):  
Radhouane Boughammoura ◽  
Mohamed Nazih Omri ◽  
Lobna Hlaoua
Keyword(s):  
Web Services ◽  
Web Service ◽  
Relevant Information ◽  
Deep Web ◽  
Web Based ◽  
New Approach ◽  
Visual Query ◽  
Novice Users ◽  
By Products

Deep Web is growing rapidly. More than 90% of relevant information in web comes from deep Web. Users are usually interested by products which satisfy their needs at the best prices and quality of service .Hence, user’s needs concerns not only one service but many competitive services at the same time. However, for commercial reasons, there is no way to compare all web services products. Each web service is a black box which accepts queries through its own query interface and returns results. As consequence, users ask separately different web services and spend a lot of time comparing products in order to find the best one. This is a burden for novice users. In this paper, the authors propose a new approach which integrates query interfaces of many web services into one universal web service. The new interface describes visually the universal query and is used to ask many web services at the same time. The authors have evaluated their approach on standard datasets and have proved good performances.

An Novel Web Service Clustering Approach for Linked Social Service

2016 ◽  
pp. 583-595
Author(s):  
Wuhui Chen ◽  
Banage T. G. S. Kumara ◽  
Takazumi Tanaka ◽  
Incheon Paik ◽  
Zhenni Li
Keyword(s):  
Web Service ◽  
Social Service ◽  
Clustering Approach ◽  
Service Clustering

scholarly journals A Survey on Web Service Discovery Approaches

2021 ◽  
pp. 53-60
Author(s):  
Abdelghany Mosa ◽  
◽  
◽  
Ahmed Abdelaziz
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Service Oriented Architecture ◽  
New Approach ◽  
Web Service Discovery ◽  
Loosely Coupled ◽  
Advantages And Disadvantages ◽  
Service Oriented ◽  
The Web

Service Oriented Architecture (SOA) is an approach to build distributed systems that deliver application functionality as services that are language and platform-independent. Web service is one of the fundamental technologies in implementing SOA based applications. Web services are modular, self-describing, self-contained and loosely coupled applications that can be published, located, and invoked across the web. As the number of web services is increased, finding a set of suitable web service candidates with regard to a user’s requirement becomes a challenge. Web service discovery is the process of finding the most suitable service by matching service descriptions against service requests. Various approaches for web service discovery have been proposed. In this paper, we present an overview of different approaches for web service discovery described in the literature and try to classify them into different categories. We also determine the advantages and disadvantages of each category. The goal is to help researchers to propose a new approach or to select the most appropriate existing approach for service discovery.

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