Focused crawling for automatic service discovery, annotation and classification in industrial digital ecosystems

Digital Ecosystems make use of Service Factories for service entities’ publishing, classification and management. However, before the emergence of Digital Ecosystems, there existed ubiquitous and heterogeneous service information in the Business Ecosystems environment. Therefore, dealing with the pr...

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Main Authors: Dong, Hai, Hussain, Farookh Khadeer
Format: Journal Article
Published: Institute of Electrical and Electronic Engineers 2011
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/35936
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author Dong, Hai
Hussain, Farookh Khadeer
author_facet Dong, Hai
Hussain, Farookh Khadeer
author_sort Dong, Hai
building Curtin Institutional Repository
collection Online Access
description Digital Ecosystems make use of Service Factories for service entities’ publishing, classification and management. However, before the emergence of Digital Ecosystems, there existed ubiquitous and heterogeneous service information in the Business Ecosystems environment. Therefore, dealing with the pre-existing service information becomes a crucial issue in Digital Ecosystems. This issue has not been addressed previously in the literature. In order to resolve this issue, in this paper we present a conceptual framework for a semantic focused crawler, with the purpose of automatically discovering, annotating and classifying the service information with the Semantic Web technologies. The technical and evaluation details of the framework are also presented and discussed in this paper.
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institution Curtin University Malaysia
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publishDate 2011
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spelling curtin-20.500.11937-359362017-09-13T15:53:33Z Focused crawling for automatic service discovery, annotation and classification in industrial digital ecosystems Dong, Hai Hussain, Farookh Khadeer service annotation service discovery service classification semantic focused crawler Digital Ecosystems service ontologies Digital Ecosystems make use of Service Factories for service entities’ publishing, classification and management. However, before the emergence of Digital Ecosystems, there existed ubiquitous and heterogeneous service information in the Business Ecosystems environment. Therefore, dealing with the pre-existing service information becomes a crucial issue in Digital Ecosystems. This issue has not been addressed previously in the literature. In order to resolve this issue, in this paper we present a conceptual framework for a semantic focused crawler, with the purpose of automatically discovering, annotating and classifying the service information with the Semantic Web technologies. The technical and evaluation details of the framework are also presented and discussed in this paper. 2011 Journal Article http://hdl.handle.net/20.500.11937/35936 10.1109/TIE.2010.2050754 Institute of Electrical and Electronic Engineers fulltext
spellingShingle service annotation
service discovery
service classification
semantic focused crawler
Digital Ecosystems
service ontologies
Dong, Hai
Hussain, Farookh Khadeer
Focused crawling for automatic service discovery, annotation and classification in industrial digital ecosystems
title Focused crawling for automatic service discovery, annotation and classification in industrial digital ecosystems
title_full Focused crawling for automatic service discovery, annotation and classification in industrial digital ecosystems
title_fullStr Focused crawling for automatic service discovery, annotation and classification in industrial digital ecosystems
title_full_unstemmed Focused crawling for automatic service discovery, annotation and classification in industrial digital ecosystems
title_short Focused crawling for automatic service discovery, annotation and classification in industrial digital ecosystems
title_sort focused crawling for automatic service discovery, annotation and classification in industrial digital ecosystems
topic service annotation
service discovery
service classification
semantic focused crawler
Digital Ecosystems
service ontologies
url http://hdl.handle.net/20.500.11937/35936