SAM: Semantic Advanced Matchmaker

UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The authors would like to thank the Research Management Centre of UTM and the Malaysian government for their support and cooperation including students and other individuals who are either directly or indirectly involved in this project. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Automatic Matchmaking of Web Services

Services oriented computing is playing a vital role in last decade to develop service oriented distributed computing systems. Web services are reusable software components on the web which can be discovered, fetched, and invoked. With an increase importance towards semantic web services, a challenging task with this domain lies in discovering, composing and then invoking on heterogeneous interface. Matching algorithms are considered basic approach to discover loosely coupled internet registry based web services but algorithms which match based on semantics of the query are limited.

Graph based, logic based and many other techniques were introduced to accomplish the task.

However, in the rest of the book we will use the terms “Web service” and and is a somewhat naïve way of matchmaking.1 With the advent of Semantic Web (SW) requirements, and (c) the “intelligence” of the service matchmaking algorithm.

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Matchmaking of Semantic Web Services Using Semantic-Distance Information

Over the last decade, a great amount of effort and resources have been invested in the development of Semantic Web Service SWS frameworks. Numerous description languages, frameworks, tools, and matchmaking and composition algorithms have been proposed. Nevertheless, when faced with a real-world problem, it is still very hard to decide which of these different approaches to use.

Even though the retrieval of Web services based on semantic information SWS retrieval is based on a matchmaking engine, i.e., an algorithm that finds.

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Information Services for Dynamically Assembled Semantic Grids

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However, in the rest of the book we will use the terms “Web service” and and is a somewhat naïve way of matchmaking.1 With the advent of Semantic Web (SW) requirements, and (c) the “intelligence” of the service matchmaking algorithm.

This paper mainly focuses on proposing efficient and extensible matchmaking architecture. Current matchmaking architectures and algorithms lack vision and they are unable to use all available information. However, our proposed architecture uses information such as path-length of the ontological tree nodes and partial results sets for composing required service even no exact match is found.

Semantic-distance information may be used as selection criteria and it provides accuracy in service selection. To define concept-similarity rating by ontology managers or local users may provide a way for service selection. We can then gather second level of information other than the pre-defined match levels such as exact, subsume or plug-in.

We exploit this fact in our architecture and algorithm by providing a layered and extended architecture. Different filtering layers and different specifications are applied in the matchmaking to give an extendable architecture. Skip to main content Skip to sections. This service is more advanced with JavaScript available.

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Making the web more beautiful, fast, and open through great typography. Convert nickname to fancy symbols. well yes, a note symbol is more semantic, if you’ve Pseudocode is a kind of structured english for describing algorithms. providing you the beverage equipment and equipment service your business required.

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YASA-M : a semantic Web service matchmaker

To browse Academia. Skip to main content. Log In Sign Up. Download Free PDF. Object-oriented similarity measures for semantic web service matchmaking Web Services, Nick Bassiliades.

The results of the matching algorithm are recorded using S3 test collection, Key words: Semantic Web service SAWSDL Hybrid matching Degree of match.

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D3 Polygon Intersection

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Index Terms—Semantic Web Services; Service Discovery; Service Composition Framework; Service Composition search algorithm to extract the best composition matchmaking to discover relevant services and performs.

To overcome the limitation of the basic algorithm, we must apply semantic. In this paper, we first explore. We then. Finally, the experimental results demonstrate that our similarity measure is effective. Service-oriented computing SOC [9] is a distributed computing paradigm that makes use of. A service is a loosely coupled. The key idea is to represent the functionality of a web service explicitly, using. Several description frameworks exist that annotate SWS with. The pr of ile of a service.

Zhang et al. Inputs and outputs of a service are. A SWS matchmaking algorithm should. In the basic matchmaking algorithm proposed in literature [8], an advertised service. It defines four degrees of matching: Exact, Plugin, Subsume, and Fail.

Jimmy Wales on the Semantic Web