Research On Service Matching In Semantic Grid Environment

Service as an autonomous, open and platform-independent network-based component, it makes the distributed application systems with better reusability, flexibility and growth. As the foundations of modern service science, service computing has become a cross-discipline subject that covers the science and technology of bridging the gap between Business Services and IT (Information Technology) Services. An IT-enabled business service is typically characterized by two features: its service operation model and its service charge model. A service operation model defines how the service is to be discovered and delivered, including services modeling, services creation, services discovery, services composition, service delivery, services management, et, al; a service charge model specifies how the delivered service is to be charged.Semantic Grid combines semantic Web with Grid computing technologies, and it is an extension of the current Grid technique, in which information and services are given well-defined meaning, better enabling computer and people to work in cooperation. Semantic Grid is a distributed, heterogeneous and open system, with a high degree of automation, which supports flexible collaboration and computation on a global scale. Semantic Grid makes resources be effectively sharing and efficiently processing.Service discovery in Semantic Grid environment is a fundamental research issues in answering the questions of how a service requester finds the services needed to solve its particular problem and how a service provider makes potential service requesters aware of the services it can offer. Service discovery defines a process for locating service providers and retrieving service descriptions. The problem of service discovery arises through the heterogeneity, distribution and sharing of the resources/services proposed by different virtual communities in Semantic Grid. At the heart of the service discovery is the concept of service matching. The ultimate goal of service matching is finding the similarity between different services, The more similar the two services, the more matching between them. Vice versa, the less similar the two services, the less matching between them.Service matching is a key role in the processing of service discovery and delivery. It can not provide satisfied service to users if we can not implement service matching strategy effectively. UDDI (Universal Description, Discovery, and Integration) is a public registry of published services, and it provides an efficient, flexible and extensible mechanism for service publication and discovery. However, it is a keywords-based matching strategy, and it can not provide semantic-based service matching. This, in majority of the cases, leads to low precision of the retrieved services. The reason might be that the query keywords are semantically similar but syntactically different from the terms in service descriptions. Another reason is that the query keywords might be syntactically equivalent but semantically different from the terms in the service description. Another problem with keyword-based service matching is that they cannot completely capture the semantics of users' queries, because they do not consider the relationship between the keywords semantically. One feasible solution for this problem is to use semantic-based service matching method. If we can solve service matching problems semantically, it will greatly improve the success rate of service matching and advance the extent of information sharing. Therefore, the research on semantic matching of services has important theoretical significance and a certain degree of practical value.For realizing semantic-based service matching, a more ideal approach is to use the knowledge representation model with semantic expressing capability, such as ontology, resource space model, in the processing of service matching. Semantic matching mechanism allows a powerful and flexible service discovery process as it uses semantic service descriptions. Using semantics allows to reason on values which is not only based on type reasoning, it furthermore allows subsumption reasoning. This means that the service matching is very powerful as not only a service name matching is performed. Services which would have never been found with the "keywords" service matching methods can get discovered.On the basis of the state of the art of service matching at home and abroad, integrating with the features of Semantic Grid, Semantic Web, and Web Ontology Language for Service (OWL-S), we do some research on service matching problem in Semantic Grid environment based on ontology and resource space model.The major research works and contributions of the dissertation arc as follows:(1) An extensible cosine similarity measure for service matching semantically based on ontology hierarchical structure and vector space model.Finding similarity between service entities for realizing better cooperative service is an important issue in service matching domin. Entities being compared often are modeled as sets, with their similarity traditionally determined based on set intersection. Intersection-based measures do not accurately capture similarity between entities in certain domains. On the basis of the current research, integrating with ontology hierarchical structure and vector space model, we propose an extensible cosine similarity measure for service matching. This method can capture semantic similarity among services, and the captured semantic similarity is more in line with people's intuition. The method provides a valuable reference for realizing the semantic-based service matching. In addition, we provide experimental comparison of our measure against traditional similarity measures. The results verify the better accuracy of our method. According to the theoretical research results of cognitive psychology, we also report on a user study that evaluate how well our method matches human intuition through comparing the results of our method with the psychological evaluation of investigated crowds.(2) A service similarity measure based on semantic distance.In order to accomplish semantic matching between service requests and services, or semantic matching among heterogeneous services, the important problem is that discovering the semantic similarity between service entities. The semantic similarity of services not only relate to the semantic distance between service entities, but also subsets of service entities. Another, the semantic distance and the semantic similarity have a close relationship. In order to resolve the shortage of the semantic similarity of service entities in service matching, we propose a service similarity measure method based on semantic distance on the basis of the view of information theory and the feature of ontology. The method synthetically considers the affects of inheritance relationship among concepts, as well as the position of concepts in ontology hierarchy. The acquired similarity is more reasonable, and it can more accurately simulate the original appearance of the real world. In addition, we study the semantic similarity of concept collections, and propose the maximum semantic similarity, minimum semantic similarity, average semantic similarity and weighted semantic similarity of concept collections, and demonstrate the rationality of the weighted semantic similarity measure of concept collections. At last, we provide some experimental comparison of our measure against other similarity measures. The results show how well our measure usefulness and feasibility.(3) An IOPE-based service functional matching method.In the processing of OWL-S based service matching, it is not only consider the matching of non-functional information of service, such as service name, service description, but also consider the matching of functional information of service, e.g. service inputs, service outputs, service preconditions and service effects. On the lack of the current research on the functional information matching in the processing of service matching, and combining with the Description Logic (DL), we propose an IOPE-based service functional matching method. In the method, firstly, we execute semantic matching of inputs, outputs, preconditions and effects separately. And then, we compute weighted overall similarity of service functional matching. This method can realize semantic matching based on service functional information. Through setting the weight on-demand, it makes the service matching process has high degree of flexibility. This method is a useful attempt of service functional matching in the service matching domain. At last, the evaluation of the method is done using a qualitative and a quantitative analysis, and the feasibility of this method is discussed.(4) A service similarity measure based on resource space model.A Resource Space Model (RSM) is a semantic data model for specifying, storing, managing and locating Web resources by appropriately classifying the contents of resources. Through multi-dimensional resource spaces, users can efficiently and effectively organize and manage Web resources. With the research and development of RSM, service matching based on RSM becomes a fundamental and valuable issue. The essence of RSM-based service matching is that the similarity measure between different RSMs. On the basis of the RSM's features, we propose a RSM-based service similarity measure. Through computing the similarity of coordinates, axes and resource space separately, we fulfill the similarity measure of RSM. We evaluate our method on experimental data sets, and report empirically the strength of our approach.Finally, the research works in the dissertation are summarized and the future works are presented. Although the research works of semantic-based service matching in this dissertation have some theoretical significance and potential practical value, the research works are only a small part of the whole service computing research. We will do further study on the basis of the current researches.