A Proposed Architecture for Learning Object Selection and Discovery Based on WSMO

Hanafy, Mohamed E.; Fakhry, Moutaz

doi:10.1109/nwesp.2008.30

“…Currently, there are several approaches proposed to provide semantic support to the web services as explained in [11], for example, Resource Description Framework (RDF) [12] and [13], Web Ontology Language for Services (OWL-S) [14] and [15], and Web Service Modeling Ontology (WSMO) [17]. OWL-S and WSMO are the most prominent frameworks.…”

Section: Scope and Assumptionsmentioning

confidence: 99%

QoS-based semantic web service selection

Chen¹

2021

Preprint

0

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This thesis discusses the dynamic web service selection in the semantic context with QoS constraints. The goal of this work is to investigate the mechanism of automated QoS-based semantic web service selection. Semantic Web Service (SWS) aims to achieve the automation of web service tasks, such as service discover, selection, composition and invocation. The task of semantic web service selection is further investigated through this thesis. An architecture is proposed to achieve this task by considering QoS parameters. The QoS parameters are classified into dynamic and static attributes in the architecture. The dynamic attributes are evaluated and measured as an overall value by applying utility functions. This overall value can be modeled in the semantic context for the purpose of service selection. Furthermore, the architecture directly models the static QoS attributes in the semantic context for service selection. Finally, an open SWS challenge scenario named hardware purchasing is used in several experiments in order to evaluate the proposed architecture.

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“…Currently, several frameworks have been developed to help provide the environment of building semantic projects. As mentioned in Chapter 1 of this thesis, Resource Description Framework (RDF) introduced in [12] and [13], Web Ontology Language for Services (OWL-S) introduced in [14] and [15], and Web Service Modeling Ontology (WSMO) [17] are most outstanding frameworks. These concepts will be explained in further detail in the following paragraphs.…”

Section: Comparison Between Wsmo and Other Sws Frameworkmentioning

confidence: 99%

QoS-based semantic web service selection

Chen¹

2021

Preprint

0

View full text Add to dashboard Cite

This thesis discusses the dynamic web service selection in the semantic context with QoS constraints. The goal of this work is to investigate the mechanism of automated QoS-based semantic web service selection. Semantic Web Service (SWS) aims to achieve the automation of web service tasks, such as service discover, selection, composition and invocation. The task of semantic web service selection is further investigated through this thesis. An architecture is proposed to achieve this task by considering QoS parameters. The QoS parameters are classified into dynamic and static attributes in the architecture. The dynamic attributes are evaluated and measured as an overall value by applying utility functions. This overall value can be modeled in the semantic context for the purpose of service selection. Furthermore, the architecture directly models the static QoS attributes in the semantic context for service selection. Finally, an open SWS challenge scenario named hardware purchasing is used in several experiments in order to evaluate the proposed architecture.

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“…Hanafy [18] presented a new approach -as shown in figure 4 -adding a new compact layer which functioned as an abstract layer for all learning objects hosted by some provider of learning objects (LOP) i.e. this layer represented all types and classes hosted by each LOP semantically.…”

Section: Related Workmentioning

confidence: 99%

A learning semantic Web service for generating learning paths

Mahmoud¹,

Azaiez²,

Bettahar³

et al. 2015

2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS)

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International audienceIn the present work, we presented a new approach to provide learners with learning paths adapted to their requests. These courses were generated by the composition of the learning semantic Web services. Our approach defined a learning Web service (WS) for each learning object (LO) to overcome the problems of interoperability and accessibility of learning objects. Each WS was represented, in the directory, by a learning semantic Web service (OWLS-LO) to describe a semantic understanding of the object being represented. This semantic approach was described by ontologies

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A Proposed Architecture for Learning Object Selection and Discovery Based on WSMO

Cited by 8 publications

References 6 publications

QoS-based semantic web service selection

QoS-based semantic web service selection

QoS-based semantic web service selection

A learning semantic Web service for generating learning paths

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