scholarly journals Supporting Semantically Enhanced Web Service Discovery for Enterprise Application Integration

2010 ◽  
pp. 105-130 ◽  
Author(s):  
Dimitrios Kourtesis ◽  
Iraklis Paraskakis
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Service Providers ◽  
Enterprise Application Integration ◽  
Web Service Discovery ◽  
Fine Grained ◽  
Service Characteristics ◽  
Enterprise Application ◽  
Semantically Enhanced ◽  
Essential Prerequisite

The availability of sophisticated Web service discovery mechanisms is an essential prerequisite for increasing the levels of efficiency and automation in EAI. In this chapter, we present an approach for developing service registries building on the UDDI standard and offering semantically-enhanced publication and discovery capabilities in order to overcome some of the known limitations of conventional service registries. The approach aspires to promote efficiency in EAI in a number of ways, but primarily by automating the task of evaluating service integrability on the basis of the input and output messages that are defined in the Web service’s interface. The presented solution combines the use of three technology standards to meet its objectives: OWL-DL, for modelling service characteristics and performing fine-grained service matchmaking via DL reasoning, SAWSDL, for creating semantically annotated descriptions of service interfaces, and UDDI, for storing and retrieving syntactic and semantic information about services and service providers.

Web Service Discovery for Cloud-Based Mobile Application Using Multi-Level Clustering and QoS-Based Ranking

2016 ◽  
Vol 26 (07) ◽  
pp. 1077-1097
Author(s):  
R. Kanesaraj Ramasamy ◽  
Fang-Fang Chua ◽  
Su-Cheng Haw ◽  
Chin-Kuan Ho
Keyword(s):  
Web Services ◽  
Web Service ◽  
Mobile Application ◽  
Service Discovery ◽  
Service Providers ◽  
User Preferences ◽  
Web Service Discovery ◽  
Multi Level ◽  
Searching Performance

Since many service providers are providing similar web services, finding an accurate web service based on user preferences is becoming a challenging task. Therefore, enhancing web service discovery (WSD) method will improve the searching performance. In this paper, we firstly discuss and review some existing web service discovery approaches and identify their limitations. Subsequently, we propose a web service discovery method for cloud-based mobile application by using multi-level clustering technique to improve performance by reducing the searching scope. Our web service discovery architecture is able to increase the discoverability of more accurate web services based on user's preferences. Meanwhile, user preference Quality of Services (QoS) attributes are also used for ranking procedure to allow user to decide the quality of the mobile application. The experimental results show that our approach is able to increase the searching performance and provide a reliable list of selection for users.

A Study on Different Web Service Discovery Approaches

2018 ◽  
Vol 6 (9) ◽  
pp. 311-314
Author(s):  
Rahul P. Mirajkar ◽  
Nikhil D. Karande ◽  
Surendra Yadav
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Web Service Discovery

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.

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