Semantic resource management for the web

Tane, Julien; Schmitz, Christoph; Stumme, Gerd

doi:10.1145/1010432.1010434

Cited by 22 publications

(14 citation statements)

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“…Tane et al [14] proposed an ontology-based focused crawler for an ontology management system Courseware Watchdog. Ganesh et al [15] proposed an association metric, with the purpose of optimizing the order of visited URLs for Web crawlers.…”

Section: Related Workmentioning

confidence: 99%

Automatic semantic classification and categorization of web services in digital environment

Sawant¹,

Ghorpade²

2014

International Conference on Computing and Communication Technologies

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Classification of web services through semantic service discovery of a similar event will be the feature services. However, to improve the selection and matching process is not enough. The existing service discovery approaches often published keyword matching to find web services practices. In this paper we propose a framework for automatic service classification and categorization of web service process in digital environment. The proposed framework semantically perform automated service discovery and domain selection using domainknowledge ontology based classification in a digital environment to improvise the service categorization. It is efficiently able to classify and annotated service information by means of specific service domain knowledge. In order to thoroughly evaluate the performance of our proposed semantic based crawlers for automatic service discovery, we measure the Precision, Mean Average Precision, Recall and Fmeasure Rates.

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Section: Related Workmentioning

confidence: 99%

Automatic semantic classification and categorization of web services in digital environment

Sawant¹,

Ghorpade²

2014

International Conference on Computing and Communication Technologies

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“…These crawlers are able to utilize ontology to classify web documents by computing the similarity values between ontology concepts and descriptions of URLs of web documents [19,36]. Courseware Watchdog was developed by Tane et al [32], which has one special feature whereby users can specify their preferences on certain ontology concepts by assigning corresponding weights to the preferred concepts. Then the weights of concepts are aggregated with the similarity values between concepts and web documents in order to obtain user-preferred web documents.…”

Section: Semantic Focused Crawlersmentioning

confidence: 99%

A framework for discovering and classifying ubiquitous services in digital health ecosystems

Dong

Hussain

Chang

2011

Journal of Computer and System Sciences

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A digital ecosystem is a widespread type of ubiquitous computing environment comprised of ubiquitous, geographically dispersed, and heterogeneous species, technologies and services. As a subdomain of the digital ecosystems, digital health ecosystems are crucial for the stability and sustainable development of the digital ecosystems. However, since the service information in the digital health ecosystems exhibits the same features as those in the digital ecosystems, it is difficult for a service consumer to precisely and quickly retrieve a service provider for a given health service request. Consequently, it is a matter of urgency that a technology is developed to discover and classify the health service information obtained from the digital health ecosystems. A survey of state-of-the-art semantic service discovery technologies reveals that no significant research effort has been made in this area. Hence, in this paper, we present a framework for discovering and classifying the vast amount of service information present in the digital health ecosystems. The framework incorporates the technology of semantic focused crawler and social classification. A series of experiments are conducted in order to respectively evaluate the framework and the employed mathematical model.

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“…The EM algorithm was also the method of choice in Talavera and Gaudioso (2004), where clustering was used to discover user behavior patterns in collaborative activities in e-learning applications. Some researchers (Drigas & Vrettaros, 2004;Hammouda & Kamel, 2005;Tane, Schmitz, & Stumme, 2004) propose the use of clustering techniques to group similar course materials: an ontology-based tool, within a Web Semantics framework, was implemented in Tane et al (2004) with the goal of helping e-learning users to find and organize distributed courseware resources. An element of this tool was the implementation of the Bisection K-Means algorithm, used for the grouping of similar learning materials.…”

Section: Clusteringmentioning

confidence: 99%

Evaluation of e-learning systems based on fuzzy clustering models and statistical tools

Hogo

2010

Expert Systems with Applications

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Semantic resource management for the web

Cited by 22 publications

References 0 publications

Automatic semantic classification and categorization of web services in digital environment

Automatic semantic classification and categorization of web services in digital environment

A framework for discovering and classifying ubiquitous services in digital health ecosystems

Evaluation of e-learning systems based on fuzzy clustering models and statistical tools

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