Semantic Web Service Discovery Research Articles

Proposal for an e-learning system model based on the invocation and semantic discovery of web services

Service-oriented computing (SOC) provides a new framework for designing distributed web applications and software in a flexible, scalable, and cost-effective manner. Its use is widespread to efficiently integrate existing Web services and create high value-added applications. This model, proven in various fields such as e-commerce, also shows significant advantages in the field of e-learning. This approach highlights the discovery and use of Web services listed in specialized directories. In fact, this paper proposes a framework for exploring web services associated with education. This approach is based on the application of a matching algorithm to select the services best suited to the needs of users of the online learning system, as well as the ontology of the e-learning domain and the semantic descriptions of the web services via web ontology language for web services (OWL-S).

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.

A semantic web services discovery approach integrating multiple similarity measures and k-means clustering

Web service (WS) discovery is an essential task for implementing complex applications in a service oriented architecture (SOA), such as selecting, composing, and providing services. This task is limited semantically in the incorporation of the customer’s request and the web services. Furthermore, applying suitable similarity methods for the increasing number of WSs is more relevant for efficient web service discovery. To overcome these limitations, we propose a new approach for web service discovery integrating multiple similarity measures and k-means clustering. The approach enables more accurate services appropriate to the customer's request by calculating different similarity scores between the customer's request and the web services. The global semantic similarity is determined by applying k-means clustering using the obtained similarity scores. The experimental results demonstrated that the proposed semantic web service discovery approach outperforms the state-of-the approaches in terms of precision (98%), recall (95%), and F-measure (96%). The proposed approach is efficiently designed to support and facilitate the selection and composition of web services phases in complex applications.

Визначення ступеня семантичної подібності з використанням апарату дескриптивних логік

Establishing the semantic similarity of information is an integral part of the process of solving any information retrieval tasks, including tasks related to big data processing, discovery of semantic web services, categorization and classification of information, etc. The special functions to determine quantitative indicators of degree of se­mantic similarity of the information allow ranking the found information on its semantic proximity to the pur­po­se or search request/template. Forming such measures should take into account many aspects from the mea­nings of the matched concepts to the specifics of the business-task in which it is done. Usually, to construct such si­milarity functions, semantic ap­proaches are combined with structural ones, which provide syntactic comparison of concepts descriptions. This allows to do descriptions of the concepts more detail, and the impact of syntactic matching can be significantly reduced by using more expressive descriptive logics to represent information and by moving the focus to semantic properties. Today, DL-ontologies are the most developed tools for representing semantics, and the mechanisms of reasoning of descriptive logics (DL) provide the possibility of logical inference. Most of the estimates presented in this paper are based on basic DLs that support only the intersection constructor, but the described approaches can be applied to any DL that provides basic reasoning services. This article contains the analysis of existing approaches, models and measures based on descriptive logics. Classification of the estimation methods both on the levels of defining similarity and the matching types is proposed. The main attention is paid to establishing the similarity between concepts (conceptual level models). The task of establishing the value of similarity between instances and between concept and instance consists of finding the most specific concept for the instance / instances and evaluating the similarity between the concepts. The term of existential similarity is introduced. In this paper the examples of applying certain types of measures to evaluate the degree of semantic similarity of notions and/or knowledge based on the geometry ontology is demonstrated.

Defining degree of semantic similarity using description logic tools

Establishing the semantic similarity of information is an integral part of the process of solving any information retrieval tasks, including tasks related to big data processing, discovery of semantic web services, categorization and classification of information, etc. The special functions to determine quantitative indicators of degree of semantic similarity of the information allow ranking the found information on its semantic proximity to the purpose or search request/template. Forming such measures should take into account many aspects from the meanings of the matched concepts to the specifics of the business-task in which it is done. Usually, to construct such similarity functions, semantic approaches are combined with structural ones, which provide syntactic comparison of concepts descriptions. This allows to do descriptions of the concepts more detail, and the impact of syntactic matching can be significantly reduced by using more expressive descriptive logics to represent information and by moving the focus to semantic properties. Today, DL-ontologies are the most developed tools for representing semantics, and the mechanisms of reasoning of descriptive logics (DL) provide the possibility of logical inference. Most of the estimates presented in this paper are based on basic DLs that support only the intersection constructor, but the described approaches can be applied to any DL that provides basic reasoning services. This article contains the analysis of existing approaches, models and measures based on descriptive logics. Classification of the estimation methods both on the levels of defining similarity and the matching types is proposed. The main attention is paid to establishing the similarity between concepts (conceptual level models). The task of establishing the value of similarity between instances and between concept and instance consists of finding the most specific concept for the instance / instances and evaluating the similarity between the concepts. The term of existential similarity is introduced. In this paper the examples of applying certain types of measures to evaluate the degree of semantic similarity of notions and/or knowledge based on the geometry ontology is demonstrated.

Heterogeneous ensemble learning method for personalized semantic web service recommendation

Semantic web service (SWS) discovery and recommendation (SWSR) has emerged as a potential technology which aims to fulfill the user requirements by providing an improved recommendation for Academic and business communities. In SWSR, the user search pattern is adopted to make service discovery as well as recommendation. In order to achieve the precise recommendation of SWS, the ensemble learning method is utilized. This method encompasses the elimination of in-appropriate features and selects the optimal features of the requirements for the Academic and business communities. Semantic analysis is the one of the dominant technologies for SWSR, but it has not yet been explored by applying the ensemble learning over the service features to make optimal selection of features and to provide personalized recommendation. With this motive, in this paper a Heterogeneous Ensemble Learning method for Semantic web service Personalization and recommendation (HEL-SWSR) framework has been proposed. It will revitalize the industries to select optimal services SWS discovery. HEL-SWSR assists the feature extractions, concatenation of features selection using user profiles and triples from the OWL-S files. This framework combines various methods that eventually ensure service selection through the Maximum Voting Ensemble (MVE) technique. The MVE helps to select the services and recommends the top-10 services. From that list, the Academic or Business communities can be able to predict the appropriate services. The proposed framework performance is noticeably enhanced when compared with the traditional user search pattern technique.

Leverage Label and Word Embedding for Semantic Sparse Web Service Discovery

Information retrieval-based Web service discovery approach suffers from the semantic sparsity problem caused by lacking of statistical information when the Web services are described in short texts. To handle this problem, external information is often utilized to improve the discovery performance. Inspired by this, we propose a novel Web service discovery approach based on a neural topic model and leveraging Web service labels. More specifically, words in Web services are mapped into continuous embeddings, and labels are integrated by a neural topic model simultaneously for embodying external semantics of the Web service description. Based on the topic model, the services are interpreted into hierarchical models for building a service querying and ranking model. Extensive experiments on several datasets demonstrated that the proposed approach achieves improved performance in terms of F-measure. The results also suggest that leveraging external information is useful for semantic sparse Web service discovery.

Toward automatic web service composition based on multilevel workflow orchestration and semantic web service discovery

Revolutionary development of service oriented architecture (SOA) fosters the integration of software components from different distributed platforms and facilitates modelling of web service composition. However, the existing four-stage framework used for automatic web service composition (AWSC), which has drawbacks and incompleteness in terms of dynamic change of user goals, adopting the functional properties for describing the behaviour of candidate services and implementing unified composition framework. To overcome these drawbacks, we propose a framework for automatic and dynamic service composition that requires adding advances of semantics to web services and modifying the existing four-stage framework. We provide a multilevel workflow orchestration using an enhanced five stage composition framework for achieving the dynamic change of user requests and scalability for automatic composition. This proposed framework can able to handle both functional and non-functional requirements through transformation techniques for providing a feasible composition solution. Furthermore, it provides a solution for the problem of discovering and selecting the most suitable service candidates using semantic web service discovery (SWSD) mechanism.

Toward automatic web service composition based on multilevel workflow orchestration and semantic web service discovery

Revolutionary development of service oriented architecture (SOA) fosters the integration of software components from different distributed platforms and facilitates modelling of web service composition. However, the existing four-stage framework used for automatic web service composition (AWSC), which has drawbacks and incompleteness in terms of dynamic change of user goals, adopting the functional properties for describing the behaviour of candidate services and implementing unified composition framework. To overcome these drawbacks, we propose a framework for automatic and dynamic service composition that requires adding advances of semantics to web services and modifying the existing four-stage framework. We provide a multilevel workflow orchestration using an enhanced five stage composition framework for achieving the dynamic change of user requests and scalability for automatic composition. This proposed framework can able to handle both functional and non-functional requirements through transformation techniques for providing a feasible composition solution. Furthermore, it provides a solution for the problem of discovering and selecting the most suitable service candidates using semantic web service discovery (SWSD) mechanism.

An Architecture for Restful Web Service Discovery Using Semantic Interfaces

In the internet environment, web services based on representational state transfer (REST) have become the de facto standard. The addition of semantics is intended to enhance the description of web services with information that enables automatic agents to understand their data. However, the existence of different languages to semantically describe services makes it difficult to discover and select the service that best meets a requirement. Furthermore, relatively few proposals have a RESTful service semantic description, making the discovery process for RESTful services more difficult. This work proposes a RESTful semantic web service discovery architecture based on semantic interfaces (SERIN). SERIN is an ontology with annotations that semantically describe RESTful web services. This architecture enables software agents to automatically discover and make service calls in order to execute a determined task.

Ranking semantic web services by matching triples and query based on similarity measure

A Web Service (WS) provides interoperability among platforms. To find out a desired web service that matches the user requirements are difficult, which raises the need for the WS discovery tool. This paper proposes a tool for Semantic Web Service Discovery (SWSD) to calculate Semantic Web Service (SWS) similarity values between the Requested Query (RQ) and Triples (Tr) from OWL-S file. SWSD uses processing components such as Parts of Speech (POS), WordNet Dictionary, and WordNet Sense (WNS) for reducing the errors in the matching. This matching determines the similarity Threshold (T) using Matching Rules (MR), and then calculates the average T values, which are ordered and ranked. This paper also analyses results in two phases such as ranking the services and QRT. When the Tr values increases, the QRT values are also increases. The SWSD results are better than existing framework results based on the above MR, Test Case, and QRT. These will help the user to find out the desired SWS.

Frame Logic‐based specification and discovery of semantic web services with application to medical appointments

AbstractMatching web services and client requirements in the form of goals is a significant challenge in the discovery of semantic web services. The most common but unsatisfactory approach to matching is set‐based, where both the client and web services declare what objects they require and what objects they can provide. Matching then becomes the simple task of comparing sets of objects. This approach is inadequate because it says nothing about the functionality required by the client or the functionality provided by the web service. As an alternative, we use the Frame Logic as implemented in Flora‐2 to specify web service capabilities and client requirements, including their preconditions, postconditions, and ontologies, implement a logic‐based discovery agent using Flora‐2, demonstrate its usefulness in a medical appointment making scenario, and show its efficiency both theoretically and by benchmarking. The result is an expressive yet concise representation scheme for semantic web services, and a practical, efficient, powerful, and fully implemented matching engine based purely on logical inference for web service discovery, with direct applicability to Web Service Modeling Ontology and Web Service Modeling Language, because both are based on Frame Logic.

Semantic Web Services Discovery

This article describes how Web services play an important role in several fields such as e-commerce and e-health. As the number of Web services is increasing rapidly, finding the best Web service according to users' requirements becomes more challenging. The traditional method of Web service discovery is based on keyword match. Due to this, many Web services which are most relevant to the user request are left undiscoverable. Some other emergent approaches are based on semantics to improve the quality of the discovered Web services in terms of relevance and satisfaction of user's need. In this paper, the authors present a survey of existing semantic Web services discovery approaches giving priority to relevant ones. Furthermore, this paper provides a critical and comparative analysis of the studied approaches and stands out major challenges to be addressed to substantially enhance the semantic Web service discovery.

Transformation of complex type WSDL into OWL-S for facilitating SWS discovery

Web services (WS) are manually mediated with phases such as discovery, selection, composition and invocation. WS uses syntactic description available in WSDL, which are human understandable. WS discovery and selection is made through keyword matching that produces unrelated WS, which raises the problem of finding the exact WS. To overcome this issue the search can be made to produce related web services semantically for which WS should present as semantic web service (SWS). For this human understandable description should be changed into machine understandable description. In the current Web the availability of WS is more populated than SWS, which is described in OWL-S. There is a need for transformation from WS into SWS and this paper proposes a framework for conversion. The WSDL document has two types such as simple and complex. During this conversion simple type remains unchanged, because of attribute element names and complex type contains elements values that are converted into OWL-S. The conversion is implemented and evaluated. The result outperforms the similar conversion method.

Web service discovery utilizing logical reasoning and semantic similarity

SummaryWeb service discovery facilitates the implementation of complex and reconfigurable applications in service‐oriented architecture, such as service selection, composition, and provision. This paper presents an approach for semantic and automated Web service discovery. Our approach to semantic Web service discovery consists of ontology‐based service preprocessor, reasoning‐based service filter, and parameter‐based service matcher. An important feature of this approach is that the relationship among concepts in ontology is quantified and considered as an important factor in the matching process, which results in high precision and recall. Additionally, we propose a filtering method based on logical reasoning to preprocess the large amount of Web services. Through the filtering method, Web services which are feasible in logic are selected to be matched with user requests. So there is a great improvement in the run‐time performance of service discovery approach. Experiments show that our approach is feasible and effective to discover the required Web services.

Tagging augmented neural topic model for semantic sparse Web service discovery

SummarySearch engine based Web service discovery model suffers from the semantic sparsity problem due to the fact that Web services are described in short texts, which in turn leads to poor recall. To address this issue, external information that enriches the semantics of the Web service and improves discovery performance has been highly concerned. In light of this, we propose a novel Web service discovery approach that uses the neural topic model, which seamlessly integrates tagging information and word embedding for semantic sparsity problem. More specifically, instead of clustering Web services as done in most existing service discovery approaches, we use word embedding to map the words as continuous embeddings to embody external semantics of the service description. We also leverage the neural topic model in service discovery, which takes continuous word distribution as the input and interprets the Web service description as a hierarchical model. Based on the neural topic model and word embedding, we propose an efficient Web service query and ranking approach. Experiments conducted on a real‐world Web service dataset demonstrate the effectiveness of the proposed approach.

Survey on discovery of web services

Objectives: This paper is focused on survey of various discovery methods of web services. Web service discovery and service composition play an important role in the area of services computing. Methods/Statistical Analysis: There are many important research topics related to web service discovery and are classified into two categories: the syntactic web service discovery and semantic web service discovery. Syntactic based discovery involves the techniques of search based on Universal Directory Description Interface (UDDI), search based on text document, search on schema matching and software component matching. Semantic-based search is mainly based on ontology. Findings: The discovery of web service is a hot research topic for the past several years. Searching for similar web services and selecting them according to the user requirement is a key factor to satisfy consumer requests. Because of the absence of UDDI, it is not possible to get the link of the WSDL and also it is not possible to get Quality of Service (QOS) parameters unless the web service is executed. It is necessary to search the each request dynamically using search engines because of dynamic nature of the web services and due to absence of UDDI. Application/Improvements: This paper is focused on brief discussion of result of conducted research based on a dynamic search which is different from the methods specified in the literature review. It is concluded from the result that average search speed of different Web service’s discovery system is different for different types of searches. Keywords: Data Mining, Discovery of Web Services, Ontology, Universal Directory Description Interface (UDDI), Web Services

A unified algorithm to automatic semantic composition using multilevel workflow orchestration

As a result of state-of-the-art development in service oriented architecture, we need a composition framework and comprehensive algorithm to discover and compose the web services from different environments. In this paper, we present a unified semantic-oriented framework with corresponding algorithm for automatic web service composition that integrates the comprehensive process of modified multistage composition and rigor of web semantics. Our proposed unified algorithm introduces the novel features through modified five stage composition such as transformation of non-functional properties of user requirements to all stages, optimization and semantic validation of abstract workflows using workflow automata, annotating WSDL files with additional ontologies using ontology based service repository, adopting dynamic change of user requirements for discovering candidate services, and selecting most optimal services for concrete composition using non-functional properties are effectively represented. Feasible composition solution obtained for user complex requirements through semantic web service discovery mechanism for discovering and selecting the most suitable service candidates. Furthermore, our unified algorithm can provide a composition solution through wider acceptance of semantics-oriented documents such as web ontology language for services and web service modeling ontology. We evaluate the proposed unified algorithm for automatic generation of composition using our motivating scenario, namely, home loan approval inference process. We also evaluate algorithm for automated and dynamic composition on service repositories of various sizes in increasing the levels of nesting and present the performance results.

Exploiting Semantic Web Services in the Development of Context-Aware Systems

Nowadays, web services have become increasingly used and deployed in view of the benefits that they offer. Thus, the increasing of providers and service offerings as well as the features of mobile devices has made the selection of relevant services a difficult operation. In addition, the information related to the user, called context / situation, became very useful for the implementation of the eloquent choice of the sought-after functionalities. In this paper, we present a comparative study of semantic web services and service discovery approaches. Then we propose the model of a context-aware semantic service with their process of implementation. We also highlight the discovery algorithm based on the concept of context / situation.

A SEMANTIC MODEL FOR WEB SEARCH AND DATA MATCHING USING LDA

An abstract architecture for semantic web services is requisite to present a conceptual model to assist in the principled design and implementation of semantic web service applications. The architectural structure we portray stands on the shoulders of two promising technical conceptions: Web Services and the Semantic Web. Automatic discovery is acknowledged as a key task and semantic description is the foundation for automatic service discovery. We portray a novel approach for automatic discovery of semantic Web services which utilize LDA to match a user request, articulated in service discovery language, with a semantic Web service description. Our approach to semantic based web service discovery entails semantic-based service categorization and semantic enhancement of the service request. We intend a solution for accomplishing functional level service categorization based on an ontology framework. Furthermore, we exploit clustering for precisely categorizing the web services based on service functionality.