In the last decade, the World Wide Web has been evolving as a data infrastructure, where a wide variety of resources is increasingly being made available as Web services. This trend is pushing the researchers to investigate approaches like composition platforms, aimed at empowering end users to access, compose and use these services. Despite the wide availability of data sources, due to the specific and diverse end users' information needs often no data source can satisfy these needs. This limits the adoption of composition platforms in real contexts and everyday use. In order to overcome this limitation, this paper presents a polymorphic data source that exploits the wide availability of information structured in the Linked Open Data cloud. To build this data source, a semi-automatic annotation algorithm is presented that creates semantic annotations for services available in a composition platform. An implementation of this approach in a mashup platform is described.
Enhancing Workspace Composition by Exploiting Linked Open Data as a Polymorphic Data Source
Desolda, Giuseppe
2015-01-01
Abstract
In the last decade, the World Wide Web has been evolving as a data infrastructure, where a wide variety of resources is increasingly being made available as Web services. This trend is pushing the researchers to investigate approaches like composition platforms, aimed at empowering end users to access, compose and use these services. Despite the wide availability of data sources, due to the specific and diverse end users' information needs often no data source can satisfy these needs. This limits the adoption of composition platforms in real contexts and everyday use. In order to overcome this limitation, this paper presents a polymorphic data source that exploits the wide availability of information structured in the Linked Open Data cloud. To build this data source, a semi-automatic annotation algorithm is presented that creates semantic annotations for services available in a composition platform. An implementation of this approach in a mashup platform is described.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.