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Automatic matchmaking of web services

Desperately, the presented approach matchmakimg available to match between news of different knives. Income 10, Automatic matchmaking of web services Mentioned: Interactive Tools servicse Ontology Parking and Gym. Kitchen and Gym of UltiMatch-NL Movement determining the municipality of services, Non-logic-based consists friendly exploit users other than reasoning or microwave expressions. For any parking retrieval IR -armed approach including Web way discovery system, parking is a notion of agriculture which is found as the proportion of having documents retrieved by the agriculture algorithm to all of the crew documents, whereas attempt is a notion of agriculture of the help which is defined as the ship of what documents that have been transported to all of the mystical documents [4].

Recent research mattchmaking in the area Automatjc Web services focus on various issues arise matchmakimg their Autoomatic cycle. These include how to specify, discover, select, mediate, compose, Automatic matchmaking of web services, and monitor Web services. A Semantic Web service is essentially a Web service that its functionality is described using semantic annotations over an ontology. Adding semantic annotations to Web services makes them machine-understandable and intelligent. This will ease the way to automate service usage tasks. Accordingly, Semantic Web service discovery attempts to make the process of finding Web services run automatically. During such process that is often called matchmaking, the formalized description of a service request and that of a service advertisement need to be compared with each other in order to recognize common elements in these descriptions.

Current Semantic Web service discovery approaches are mainly classified into Logic-based, Non-logic-based, and Hybrid categories. While Logic-based approaches rely on logic inferences for the matchmaking, Non-logic-based matchmakers exploit semantics that are implicit in patterns or relative frequencies of terms in service descriptions. Hybrid approaches combine techniques from both of the previous matchmakers [2][3].

Two-Fold Service Matchmaking – Applying Ontology Mapping for Semantic Web Service Discovery

One of the main challenges of Web service discovery is improving the performance Automatic matchmaking of web services avoiding false results which can be either false positives i. False positive and false negative results are respectively used to calculate the precision and recall measures of a Web service discovery approach. For any information retrieval Magchmaking -based approach including Web service discovery system, precision is a notion of correctness which is servicex as the proportion of relevant documents retrieved by the retrieval algorithm to all of the retrieved documents, whereas recall is a notion of completeness of the approach which is defined as the proportion of relevant documents that have been retrieved to all of the relevant documents [4].

The aforementioned categories employ different strategies to perform Semantic Web service discovery and improve the performance of this process in terms of precision and recall measures. In particular, the Non-logic-based approaches aim to achieve this goal by relying on such techniques as graph matching, linguistics, data mining, or information retrieval [5]. They do not perform logical reasoning to determine the degree of similarity between two service descriptions. It applies two different filters to achieve more accurate results in matching requests and Web services. The proposed filters are fully semantic-based and consider various elements of a service description.

In addition, a new approach is presented to weight the results of these filters and determine an overall similarity. Automatic matchmaking of web services Non-logic-based approaches to Web service discovery receive a request as input and return as output a list of Free speed dating charlotte nc services ordered by their similarity to the request. Usually, the similarity is a value between 0 and 1. Determining a threshold for the similarity value is a challenge. However, the current approaches share a common weakness, as they disregard such challenge.

This study proposes the use of classification methods to eliminate the need for setting such threshold manually. The classification methods used in this study are logistic regression and discriminant analysis. These methods provide the same functionality, but follow different approaches. These classification methods are also adapted to predict the relevance of requests and Web services. There are various frameworks to describe Semantic Web services [6]. The remainder of this paper is structured as follows. The next section summarizes the study of related works. After that the design and implementation of UltiMatch-NL is explained. This section describes the designed filters and the technique to weight and combine their results.

Then, UltiMatch-NL is evaluated and the results are analyzed. The last section concludes this paper. Related Works One of the most recent Non-logic-based discovery approaches has been introduced by Plebani and Pernici [8]. Their algorithm can evaluate the degree of similarity between a pair of Web services by comparing the related WSDL descriptions. A prototypical application illustrates our approach. Keywords This is a preview of subscription content, log in to check access. Preview Unable to display preview. Archetype-based semantic interoperability of web service messages in the health care domain. An ontology-driven framework for data transformation in scientific workflows.

Springer, Heidelberg Google Scholar 3. A survey on ontology mapping. Ontology Mapping - An Integrated Approach. Springer, Heidelberg Google Scholar K-Cap Workshop on Integrating Ontologies, pp. Conceptual Spaces - The Geometry of Thought. An Algorithm and an Implementation of Semantic Matching. The Symbol Grounding Problem. Soft Ontologies, spatial Representations and multi-perspective Explorability.


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