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Locating the desired web service to a client requirement is an onerous task as many web services are ready to satisfy a request. Recommending the pertinent web service and not providing the unwarranted service are the two main issues to be addressed in web service selection process. The limitation of keyword search is
images with their surrounding text are collected from a few photo forums to support this approach. The entire process is formulated in a divide-and-conquer framework where a query keyword is provided along with the uncaptioned image to improve both the effectiveness and efficiency. This is helpful when the collected data
With the advent of Web 2.0, RESTful web services are becoming increasingly popular to emphasize the web as platform. There are already many RESTful web services and the number of services is increasing rapidly. Thus, it can be difficult to find specific services using keyword based retrieval. To solve this problem, a
Web service discovery is a vital problem in service computing with the increasing number of services. Existing service discovery approaches merely focus on WSDLbased keyword search, semantic matching based on domain knowledge or ontologies, or QoS-based recommendations. The keyword search omits the underlying
called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic
Users usually have different prospective even they input a same keyword to search Web services. It is a challenge to personalize web service search engine as more and more keyword-like Web services becoming available on Internet. User interest plays an important role in personalizing search result. Therefore, through
keyword search. Since the service crawler will periodically make repeated runs to find new service descriptions or to check the status of already crawled services, the framework is of an inherently dynamic nature. Hence, it is critical to keep track of various entities like visited URLs, already added services and
Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based
keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to
With the rapid growth of web services, web services discovery becomes exceedingly important and challenging. Currently, many discovery approaches have been proposed such as keyword-based or VSM-based syntactic matching and ontology-based semantic matching. Syntactic matching approaches are clearly insufficient due to
Curse of dimensionality is a major difficulty with the classic optimization methods for high dimensional applications in which the problem size grows rapidly and mostly exponential with the number of space. In this work we present a simple yet effective multi-agent approach to apply distributed particle swarm optimization to meet such demand. Lip detection in color images, as a high-dimensional problem,...
as the services management. Existing methods for Web services clustering mostly focus on utilizing directly key features from WSDL documents, e.g., input/output parameters and keywords from description text. Probabilistic topic model Latent Dirichlet Allocation (LDA) is also adopted, which extracts latent topic features
user's request, the user has to construct the request using the keywords that best describe the user's objective and match correctly with the Web Service name or location. Clustering Web services based on function similarities would greatly boost the ability of Web services search engines to retrieve the most relevant Web
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