Semantic Web Service Matchmaking Research Articles

SDMM Based Ranking Mechanism for Semantic Web Services Search

For search of semantic Web services, a semantic Web services matching results ranking mechanism based on SDMM (semantic distance metric model) is proposed. The calculation of semantic similarity measure can be realized by using this three-dimensional SDMM which is for presenting the semantic relationship of objects defined in ontology, therefore, the semantic Web Service matchmaking results can be ranked in accordance with the semantic similarity measure. The approach based on SDMM significantly improves search accuracy of semantic Web service matchmaking, and enhance users experience of semantic Web services search. By a set of experiments, we demonstrate the benefits and effectiveness of our approach.

Semantic web services matchmaking: Semantic distance-based approach

The semantic web services (SWSs) discovery is the process of finding a service that can possibly satisfy the user requirements, choosing between several services, and composing services to form a single service. The matchmaking between the user request and SWSs is the main task of any SWSs discovery mechanism. In this paper, a semantic distance-based matchmaking algorithm (SDMA) is introduced. The idea behind SDMA is to use the measurement of the semantic distance between the user request and the tested service as an indicator of the degree of relevance between them. In addition to many proposals, a new concepts tree is proposed to perform the process of computing the semantic distance between two concepts. SDMA bases on many concept-to-concept semantic distance measures that are modified and adapted. A deep evaluation process is performed to validate the great impact of using semantic distance measures in the field of SWSs matchmaking.

A logistic regression model for Semantic Web service matchmaking

Semantic Web service matchmaking, as one of the most challenging problems in Semantic Web services (SWS), aims to filter and rank a set of services with respect to a service query by using a certain matching strategy. In this paper, we propose a logistic regression based method to aggregate several matching strategies instead of a fixed integration (e.g., the weighted sum) for SWS matchmaking. The logistic regression model is trained on training data derived from binary relevance assessments of existing test collections, and then used to predict the probability of relevance between a new pair of query and service according to their matching values obtained from various matching strategies. Services are then ranked according to the probabilities of relevance with respect to each query. Our method is evaluated on two main test collections, SAWSDL-TC2 and Jena Geography Dataset(JGD). Experimental results show that the logistic regression model can effectively predict the relevance between a query and a service, and hence can improve the effectiveness of service matchmaking.

An automated approach to Semantic Web Services Mediation

Semantic Web Services (SWS) aim at the automated discovery, selection and orchestration of Web services on the basis of comprehensive, machine-interpretable semantic descriptions. However, heterogeneities between distinct SWS representations pose strong limitations w.r.t. interoperability and reusability. Hence, semantic-level mediation, i.e. mediation between concurrent semantic representations of services, is a key requirement to allow SWS matchmaking algorithms to compare capabilities of distinct SWS. Semantic-level mediation requires to identify similarities across distinct SWS representations. Since current approaches rely either on manual one-to-one mappings or on semi-automatic mappings based on the exploitation of linguistic or structural similarities, these are perceived to be costly and error-prone. We propose a mediation approach enabling the implicit representation of similarities across distinct SWS by grounding these in so-called Mediation Spaces (MS). Given a set of SWS and their respective MS grounding, a general-purpose mediator automatically computes similarities to identify the most appropriate SWS for a given request. A prototypical application illustrates our approach.

A hybrid approach to semantic web services matchmaking

Deploying the semantics embedded in web services is a mandatory step in the automation of discovery, invocation and composition activities. The semantic annotation is the “add-on” to cope with the actual interoperability limitations and to assure a valid support to the interpretation of services capabilities. Nevertheless many issues have to be reached to support semantics in the web services and to guarantee accurate functionality descriptions. Early efforts address automatic matchmaking tasks, in order to find eligible advertised services which appropriately meet the consumer’s demand. In the most of approaches, this activity is often entrusted to software agents, able to drive reasoning/planning activities, to discover the required service which can be single or composed of more atomic services. This paper presents a hybrid framework which achieves a fuzzy matchmaking of semantic web services. Central role is entrusted to task-oriented agents that, given a service request, interact to discover approximate reply, when no exact match occurs among the available web services. The matchmaking activity exploits a mathematical model, the fuzzy multiset to suitably represent the multi-granular information, enclosed into an OWLS-based description of a semantic web service.