Web Service Discovery Process Research Articles

Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques

We propose a multi-layer data mining architecture for web services discovery using word embedding and clustering techniques to improve the web service discovery process. The proposed architecture consists of five layers: web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the web services documents. In the second layer, Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for web services representation. In the third layer, four distance measures, namely, Cosine, Euclidean, Minkowski, and Word Mover, are considered to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between web services documents. Finally, in the fifth layer, three clustering algorithms, namely, affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated for clustering of web services based on observed similarities in documents. We demonstrate how each component of the five layers is employed in web services clustering using randomly selected web services documents. We conduct experimental analysis to cluster web services using a collected dataset consisting of web services documents and evaluate their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better than those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different word representations models and syntactic and semantic similarity measures, we found that the affinity propagation clustering technique performed better in discovering similarities among Web services.

Cross-Layer Routing Based on Semantic Web Services Discovery with Energy Evaluation and Optimization in MANET

The web services discovery process in mobile adhoc networks is considered as a very difficult challenge due to the continuous change in the topology of the network and also the lack of a fixed central directory for publishing web services. Several approaches have been proposed which are based on either keywords or identifiers representing the service to be searched or by using a specific scenario of discovery. All of those proposed solutions try to respect the constraints of ad hoc networks such as energy, bandwidth, throughput ... etc. In this paper we present our new proposed model for measuring the cost of the overall energy consumption in ad hoc networks depending on the web services discovery protocols. We also present a new optimized web services discovery protocol in MANET based on cross_layer routing techniques with the dissemination in the routing process at the same time the semantic web services information and a Discovery_Diameter parameter that we have proposed to limit the area of discovery in the network. Finally, we present simulation results of our defined approach showing a significant optimization of the energy consumption level and the average throughput.

GENETIC ALGORITHM - OPTIMIZED GATED RECURRENT UNIT (GRU) NETWORK FOR SEMANTIC WEB SERVICES CLASSIFICATION

In the current era, as there is an abundant expansion of functionally similar web services, it becomes a prodigious issue for the web service discovery process. The service classification plays a significant role to greatly reduce the search space and retrieves the desirable service quickly and accurately. The classification is performed using the functional values. Recent research activities recommend RNN (Recurrent Neural Network) deep learning algorithms for efficient classification process. The state-of-the-art of GRU (Gated Recurrent Unit) one of the RNN model, provides a proficient classification process. However, the ratio of training and testing dataset, and hyperparameters namely neural network size, and batch size etc, affects the classification accuracy. The objective of the paper is to incorporate GRU model for efficient classification process. The novelty of the proposed model lies in implementing the GRU model for semantic web service classification. Furthermore, the genetic algorithm is used to predict the optimal ratio of training and testing dataset and optimal hidden neural Network units of GRU model in order to attain optimal classification. The experimental results exemplifies that the semantic web service classification is efficiently deliberated using the proposed GA-GRU model that outperforms the classification process as compared with the conventional semantic extraction using accuracy, precision, F-measure, recall and FDR (False Date Rate) rate.

Improved machine learning classifiers combined with a stochastic local search for Web services classification

The Web services classification is the process that automatically assigns a category from a list of predefined categories to the Web service described as a WSDL document and where the purpose is to improve the Web services discovery process speed. The aim of this paper is to propose an optimization approach based on the attributes selection of Web services descriptions, to automatically classify Web services found in UDDI registers in predefined categories. The proposed approach combines the meta-heuristic of Stochastic Local Search (SLS) with a supervised learning method. The purpose of this work is to optimize the classification rate of the classifier by choosing the relevant attributes that best represents the Web service. First, we propose a classification method that uses a stochastic local search (SLS) for the attributes selection, then, in a second phase, the approach calls for a supervised classification method to perform the classification task. To this end, we studied six well-known classifiers which are: Support Vector Machine (SVM), Naïve Bayes (NB), k-Nearest Neighbors (k-NN), Bayesian Network (BN), Random Tree (RT), and Random Forests (RF). The six hybrid methods which are: SVM+SLS, NB+SLS, k-NN+SLS, BN+SLS, RT+SLS, and RF+SLS are evaluated on seven real datasets. The results are interesting and demonstrate the benefits of the proposed approaches for Web service classification.

Development of a Smart Technique for Mobile Web Services Discovery

Web service (WS) presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited environment. With the increasing demand for mobile WS (MWS), the WS discovery process has become a significant challenging point in the WS lifecycle that aims to identify the relevant MWSs that best match the service requests. This discovery process is a resource-consuming task that cannot be performed efficiently in a mobile computing environment due to the limitations of mobile devices. Meanwhile, a cloud computing can provide rich computing resources for mobile environments given its unlimited and easily scalable resources. This paper proposes a semantic WS discovery and invocation framework in mobile environments based on cloud and a relationship-aware matchmaking algorithm. The discovery algorithm enriches MWS and user requests semantically with the functional and non-functional properties of Ontology Web Language for Services, such as Quality of Web Service, device context, and user preferences. The WS repository is filtered based on logical reasoning and a parameter-based matching algorithm to minimize the matching space and improve runtime performance. The cosine similarity between the user request and services repository is then assessed to generate the most relevant WS. The relationships among concepts in the ontology are considered to improve the recall and precision ratio. After the WS discovery process, users can invoke and test these services in a mobile environment through a dynamic user interface. The interface of the invocation process is changed according to the WS description document. An application prototype is also developed to evaluate the framework based on a Cordova cross-mobile development framework.

Enhancing web service clustering using Length Feature Weight Method for service description document vector space representation

Due to the rapid growth of web services in repositories, discovering the requisite web service is becoming increasingly cumbersome task. It has raised the demand for efficient web service clustering algorithms. In service repositories, when related web services are stored in a clustered way, it enhances the web service discovery process by reducing search space and time. Many eminent researchers have worked in this field and used the Term Frequency – Inverse Document Frequency (TF-IDF) method for representing web services in vector space. In general, there are various limitations of the TF-IDF approach i.e. (1) Not efficient for large documents (2) Position of term and its co-occurrences does not matter (3) Unable to analyze how terms are dispersed in different documents. In the web service scenario, services are represented in short text form. TF-IDF does not work well in web service representation because of the reason that it is unable to effectively find the importance of a term concerning its occurrence in other documents. If we compare two service documents i.e. ‘s1’ and ‘s2’ first having a large and second having small number of terms respectively then TF-IDF does not demonstrate the importance of terms in ‘s1’ as smaller to ‘s2’. Therefore, it is not possible to assign effective weights to the terms. In the lack of effective vector space representation, the performance of the clustering algorithm also degrades. In this paper, we propose a new approach i.e. LFW+K which is based on Length Feature Weight (LFW) for the vectorized representation of service followed by K-Means clustering. The proposed approach helps to find the informative term from web service and assigns the term weight accordingly by considering parameters like the dimension of the web service document, maximum frequency of a term in the document and occurrences of a term in other documents. LFW+K is applied on the datasets of real-world web services and the performance is measured using standard measurement criteria (i.e. precision, recall, F1-score, and accuracy). Results of the proposed approach are compared with K-Means clustering on TF-IDF representation method i.e. TF-IDF+K. Results show that the proposed method outperforms the clustering done by using TF-IDF method for vector space representation of web services.

Evaluation of web service clustering using Dirichlet Multinomial Mixture model based approach for Dimensionality Reduction in service representation

In recent years, mainly the functionality of services are described in a short natural text language. Keyword-based searching for web service discovery is not efficient for providing relevant results. When services are clustered according to the similarity, then it reduces search space and due to that search time is also reduced in the web service discovery process. So in the domain of web service clustering, basically topic modeling techniques like Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Hierarchical Dirichlet Processing (HDP), etc. are adopted for dimensionality reduction and feature representation of services in vector space. But as the services are described in the form of short text, so these techniques are not efficient due to lack of occurring words, limited content, etc. In this paper, the performance of web service clustering is evaluated by applying various topic modeling techniques with different clustering algorithms on the crawled dataset from ProgrammableWeb repository. Gibbs Sampling algorithm for Dirichlet Multinomial Mixture (GSDMM) model is proposed as a dimensionality reduction and feature representation of services to overcome the limitations of short text clustering. Results show that GSDMM with K-Means or Agglomerative clustering is outperforming all other methods. The performance of clustering is evaluated based on three extrinsic and two intrinsic evaluation criteria. Dimensionality reduction achieved by GSDMM is 90.88%, 88.84%, and 93.13% on three real-time crawled datasets, which is satisfactory as the performance of clustering is also enhanced by deploying this technique.

An optimised semantic web services discovery in MANET

The web services discovery process in mobile ad hoc networks is considered as a very difficult challenge due to the continuous change in the topology of the network and also the lack of a fixed central directory for publishing web services. Several approaches have been proposed, all these solutions are based on either keywords or identifiers representing the service to be searched or by using a specific scenario of discovery, all by trying to respect the constraints of ad hoc networks such as energy, bandwidth, throughput, etc. In this paper, we will present a new web services discovery protocol based on the dissemination of semantic web services information in routing process using the cross-layer routing techniques. We will also present the simulation results obtained where we have noticed a significant optimisation of the energy consumption level and of the average throughput using the DISCOVERY_DIAMETER parameter that we have proposed to limit the area of discovery in the network.

A semantic similarity measure integrating multiple conceptual relationships for web service discovery

The process of Web service discovery identifies the most relevant services to requesters’ service queries. We propose a new measure of semantic similarity integrating multiple conceptual relationships (SIMCR) for Web service discovery. The new measure enables more accurate service-request comparison by treating different conceptual relationships in ontologies such as is-a, has-a and antonomy differently. Each service or request is represented by vectors of terms (or words) that characterize both the interface signature and textual description. The overall semantic similarity is computed as a weighted aggregation of interface similarity and description similarity. The experimental results confirm the effectiveness of the proposed semantic similarity measure. As demonstrated in this study, the semantic Web service discovery method based on the proposed similarity measure outperforms existing state-of-the-art discovery methods in terms of precision, recall and F-measure. The proposed semantic similarity measure has wider applications such as to improve document classification or clustering, and to more accurately represent and apply knowledge in expert and intelligent systems.

Bio-inspired-based approach for web services classification

Web services have become a promising technology in a variety of e-science or e-business areas. Classification of web services is one methodology that can be used in order to enhance the speed of web service discovery process. There are a variety of models and methods to deal with this issue from different aspects. Bio-inspired algorithms are becoming main approaches and solutions. This paper proposes a bio-inspired-based approach for web services classification. In our approach, web services are not classified based on services with similar features, but on services which share many interaction relations. This organisation can be used among others to guide the search algorithms compositions. Ant-based clustering algorithms are an appropriate alternative to traditional clustering algorithms. The algorithm has a number of features that make it an interesting study of cluster analysis. It has the ability of automatically discovering the number of clusters.

Novel Semantic Model for Intelligent Discovery of Web Services

Main challenges to the current semantic web service technologies are exponential continuous growth in the number of services on the Internet, syntax based discovery, lack of common agreed upon semantic service standards and heterogeneity of ontologies. In this paper, a service discovery approach independent of semantic service description models is proposed to solve the challenges of the current web service discovery. The idea is to combine principles from machine learning, data mining, statistical techniques and measures of semantic relatedness to make the semantic web service discovery process more intelligent, efficient and effective. The proposed approach exploits the use of semantic as well as syntactic information present within the service description profiles. Our approach is unique in terms of its application to any web service description language and the use of Omiotis measure of semantic relatedness for service discovery. The proposed approach has been implemented on OWL-S based service descriptions profiles and is able to find semantic relationship between the services which were otherwise discarded by the OWL-MX matchmaker. Empirical analysis shows that the proposed method out performs the

A Dynamic Reputation-Based Approach for Web Services Discovery

Web services discovery is a crucial process in the service-oriented architecture (SOA). This process depends on the functional and non-functional (Quality of services (QoS)) properties of the published services. Functional properties present the objectives of the service and the quality of the service indicates the performance of the service. Reputation of the services is one of the important QoS properties. Assigning robust and precise reputation values for the service affects positively in the output of the web services discovery process by returning the most relevant services for the consumer. In this paper, we propose an approach for updating reputation of the Web services based on the trust factors of the consumers and two pre-defined thresholds, reputation threshold, and agreement threshold. The proposed approach is implemented and tested on a set of services. The results show that the proposed reputation updating approach is powerful and gives robust and precise results in a dynamic way. As a consequence, the web service discovery process which is based on the proposed approach gives a robust and precise results in a dynamic way.

Cross-Language Semantic Web Service Discovery to Improve the Selection Mechanism by using Data Mining Techniques

Web service became one of the important methods for communication through the internet and its usage increased in the levels of users and developers. Semantic web service represents the second generation of web services that contains more description and information about its contents. Searching and dealing with web service is done through process called web service discovery which returns a Semantic Web Service Description Language file (SWSDL) for each web service. This research aims to expand the semantic web service usage through adding the multilanguage capability to the web service’s discovery process and through recommending other web services to the user based on his history in using web services. These aims were achieved by modifying the web service discovery model through adding two important techniques the Cross Language Information Retrieval (CLIR) technique and the data mining association rules technique. This research proposed two sub models, the first sub model proposed the application of CLIR techniques and information retrieval method to support Bilingual Web service discovery process the second language that proposed here is Arabic. Text mining techniques were applied on SWSDL content and user’s query to be ready for CLIR methods, this sub model was tested on a curated catalogue of Life Science Web Services http://www.biocatalogue.org/ and achieving 99.38 % accuracy and 87.23 precision of the effectiveness of the monolingual system. The second sub model proposed a process of web service recommendation by applying the data mining techniques to suggest another web service beside the one he got from the discovery process based on the user’s history. This sub model was tested on the mention curated web services site and the results were 65 % of users chose services from the services that recommended by the proposed sub model.

A Normalized Approach for Service Discovery

In today's world web services are the known perception to all the users who uses the internet. The Web Service process involves service discovery, selection and ranking. Discovery is the process of matchmaking of user query with advertisements in the repository. Our motivation is to develop a model for web service discovery with the combined approach of service selection and ranking. In this paper, we have proposed a technique for web service discovery process, combining the keyword search and semantic search and ranking the services. The implementation results show that the proposed model performs better for the web service discovery process.

Cross Language Information Retrieval Model for Discovering WSDL Documents Using Arabic Language Query

Web service discovery is the process of finding a suitable Web service for a given user’s query through analyzing the web service‘s WSDL content and finding the best match for the user’s query. The service query should be written in the same language of the WSDL, for example English. Cross Language Information Retrieval techniques does not exist in the web service discovery process. The absence of CLIR methods limits the search language to the English language keywords only, which raises the following question “How do people that do not know the English Language find a web service, This paper proposes the application of CLIR techniques and IR methods to support Bilingual Web service discovery process the second language that proposed here is Arabic. Text mining techniques were applied on WSDL content and user’s query to be ready for CLIR methods. The proposed model was tested on a curated catalogue of Life Science Web Services http://www.biocatalogue.org/ and used for solving the research problem with 99.87 % accuracy and 95.06 precision

Proposed Conceptual Model for Semantically Enabled Web Services Based On QoS

Presently, the service discovery is the process of discovering one or more documents that describe in a particular service. Most of the service discovery mechanisms perform syntactic matching. That means it supports the key word based search. This often leads to poor service discovery result. Another drawback of the existing service discovery mechanism is that the query service matching score is calculated and taking an account only the keywords from the user query's and the terms is the service descriptions. The keyword based service discovery mechanism supported by Universal Description Discovery and Integration (UDDI) and most of the existing service search engines like Yahoo and Google suffer from keyword based search. Firstly it is difficult for the user to obtain the desired services because the number of the retrieved services with respect to the keywords may be huge. Secondly, keywords are insufficient in expressing the semantic concepts and semantically different concepts could possess identical representation (homonyms), which will further low accuracy. This warrants the need to establish an effective and reliable process of web service discovery. This research has emerged to develop methods and model to improve the accuracy of the best web service discovery to match the best service. This paper presents a survey of web service discovery systems, focusing on systems that support either semantics based or syntactic based approach.

Using SOA and RIAs for water data discovery and retrieval

The main barriers to make full use of the wealth of available online data is that users are unable to rapidly locate relevant web services and retrieve appropriate data sets from different data repositories as well as efficiently reconcile integration between temporal and geospatial data. To address these issues, this paper focuses on the development of an online Water Data Discovery and Retrieval system (WDDRs) to enhance the capabilities of services discovery, data retrieval, and data visualization. The most significant features of WDDRs prototype are reflected in two aspects. On one hand, a water ontology incorporated with Universal Description, Discovery and Integration (UDDI) based enhanced services catalog offers facilities to alleviate semantic heterogeneity and associate semantic information with the web services discovery and data retrieval process. On the other hand, by embracing the capability within the context of Service Oriented Architectures (SOA) and leveraging the latest protocols of several open web service standards and two popular RIAs (Rich Internet Applications) frameworks including the Microsoft Silverlight and the ESRI ArcGIS API for Silverlight, this system provides an interactive web portal which enables users to one-stop search, access, download and visualize different types of geospatial and observational data in a single environment. With the aim of supporting the study of integrated water environmental assessment, several investigations about water data discovery and retrieval were implemented to demonstrate the feasibility and effectiveness of the WDDRs.

Extended Web Services Model Design for Non-Functional Requirements

To manage web services, it needs to monitors quality of them periodically when changing their status like availability, performance and security policy. The non-functional requirements of web services based on QoS parameters has been presented in this paper for the purpose of finding the best available web service during Web services discovery process. We also presented an extended web services framework based on SOA structure for providing information about quality of web services and build a prototype for applying quality factors.

WSMO and WSMX Support to the Semantic Web Services Technology

This paper tests the ability of the Web Service Modeling Ontology (WSMO) and the Web Service Modeling eXecution environment (WSMX) to support the Semantic Web Services technology, and automate the process of web service discovery, selection and invocation. First, it introduced web services and their limitations that were overcome in the vision of the Semantic Web Services technology. Then a Semantic Web Service (SWS) was built on top of WSMO to access the publications of the German University in Cairo (GUC), and was registered to WSMX. To test the validity to the claim, a service request to access the publications of the GUC was sent to WSMX and the process followed by WSMX was investigated. Furthermore, the discussion added a suggestion that would enhance the transparency between the Semantic Web and WSMO-WSMX initiatives.

Data Mining in Web Services Discovery and Monitoring

The business needs, 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. This article recommends several data mining applications that can leverage problems concerned with the discovery and monitoring of Web services. This article then presents a case study on applying the clustering data mining technique to the Web service usage data to improve the Web service discovery process. This article also discusses the challenges that arise when applying data mining to Web services usage data and abstract informat