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.

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.

Towards Web Service Discovery Based on Context-Aware

2012 ◽  
Vol 601 ◽  
pp. 325-331
Author(s):  
Shu Gao ◽  
Hua Huang ◽  
Bing Ge
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
The Other ◽  
Context Information ◽  
Context Aware ◽  
Web Service Discovery ◽  
Traditional Service ◽  
Domain Information ◽  
The Web

Nowadays, a lot of services which do not meet user’s the requirements are returned while searching web services with traditional service discovery, and moreover, the efficiency is very low. On the other hand, current service directory specifications do not focus on context-aware. In this paper, a novel, enhanced model for the web service discovery, which is based on context-aware, is proposed, and the context information and domain information are integrated to filter and sort services during the process of service discovery. By this way, the precision and efficiency of the service discovery can be improved.

Exploiting named entity recognition for improving syntactic-based web service discovery

2018 ◽  
Vol 45 (3) ◽  
pp. 398-415 ◽  
Author(s):  
Ignacio Lizarralde ◽  
Cristian Mateos ◽  
Juan Manuel Rodriguez ◽  
Alejandro Zunino
Keyword(s):  
Information Retrieval ◽  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Software Industry ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Web Service Discovery ◽  
Named Entity ◽  
Text Corpora

Web Services have become essential to the software industry as they represent reusable, remotely accessible functionality and data. Since Web Services must be discovered before being consumed, many discovery approaches applying classic Information Retrieval techniques, which store and process textual service descriptions, have arisen. These efforts are affected by term mismatch: a description relevant to a query can be retrieved only if they share many words. We present an approach to improve Web Service discoverability that automatically augments Web Service descriptions and can be used on top of such existing syntactic-based approaches. We exploit Named Entity Recognition to identify entities in descriptions and expand them with information from public text corpora, for example, Wikidata, mitigating term mismatch since it exploits both synonyms and hypernyms. We evaluated our approach together with classical syntactic-based service discovery approaches using a real 1274-service dataset, achieving up to 15.06% better Recall scores, and up to 17% Precision-at-1, 8% Precision-at-2 and 4% Precision-at-3.

Semantic Web Services for Simulation Component Reuse and Interoperability

2010 ◽  
pp. 336-352
Keyword(s):  
Software Engineering ◽  
Web Services ◽  
Semantic Web ◽  
Web Service ◽  
Service Discovery ◽  
Organizational Boundaries ◽  
Web Service Discovery ◽  
Novel Approach ◽  
Commercial Off The Shelf ◽  
Semantic Interoperation

Commercial-off-the-shelf (COTS) Simulation Packages (CSPs) are widely used in industry primarily due to economic factors associated with developing proprietary software platforms. Regardless of their widespread use, CSPs have yet to operate across organizational boundaries. The limited reuse and interoperability of CSPs are affected by the same semantic issues that restrict the inter-organizational use of software components and web services. The current representations of Web components are predominantly syntactic in nature lacking the fundamental semantic underpinning required to support discovery on the emerging Semantic Web. The authors present new research that partially alleviates the problem of limited semantic reuse and interoperability of simulation components in CSPs. Semantic models, in the form of ontologies, utilized by the authors’ Web service discovery and deployment architecture, provide one approach to support simulation model reuse. Semantic interoperation is achieved through a simulation component ontology that is used to identify required components at varying levels of granularity (i.e. including both abstract and specialized components). Selected simulation components are loaded into a CSP, modified according to the requirements of the new model and executed. The research presented here is based on the development of an ontology, connector software, and a Web service discovery architecture. The ontology is extracted from example simulation scenarios involving airport, restaurant and kitchen service suppliers. The ontology engineering framework and discovery architecture provide a novel approach to inter-organizational simulation, by adopting a less intrusive interface between participants Although specific to CSPs this work has wider implications for the simulation community. The reason being that the community as a whole stands to benefit through from an increased awareness of the state-of-the-art in Software Engineering (for example, ontology-supported component discovery and reuse, and service-oriented computing), and it is expected that this will eventually lead to the development of a unique Software Engineering-inspired methodology to build simulations in future.

Algorithm for Web Service Discovery Based on Information Retrieval Using WordNet and Linear Discriminant Functions

2008 ◽  
Vol 12 (2) ◽  
pp. 182-189 ◽  
Author(s):  
Ricardo Sotolongo ◽  
◽  
Carlos Kobashikawa ◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Information Retrieval ◽  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Distributed Applications ◽  
Service Description ◽  
Lexical Database ◽  
Linear Discriminant ◽  
Web Service Discovery ◽  
The One

An algorithm based on information retrieval that applies the lexical database WordNet together with a linear discriminant function is proposed. It calculates the degree of similarity between words and their relative importance to support the development of distributed applications based on web services. The algorithm uses the semantic information contained in the Web Service Description Language specifications and ranks web services based on their similarity to the one the developer is searching for. It is applied to a set of 48 real web services in five categories, then compared them to four other algorithms based on information retrieval, showing an averaged improvement over all data between 0.6% and 1.9% in precision and 0.7% and 3.1% in recall for the top 15 ranked web services. The objective was to reduce the burden and time spent searching web services during the development of distributed applications, and it can be used as an alternative to current web service discovery systems such as brokers in the Universal Description, Discovery, and Integration (UDDI) platform.

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