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Armadillo is a tool that provides automatic annotation for the Semantic Web using unannotated resources like the existing Web for information harvesting, that is: combining a crawling mechanism with an extensible architecture for ontology population. The latter is achieved via largely unsupervised machine learning, boot-strapped from oracles, such as web-site wrappers. It is backed up by ‘evidential reasoning', which allows evidence to be gained from the redundancy in the Web as well as inaccuracies in information, also characteristic of today's Web, to be circumvented. In this paper we sketch how the architecture of Armadillo has now been reinterpreted as workflow templates that compose semantic web services and show how the porting of Armadillo to new domains, and furthermore the application of new tools, has thus been simplified and benefits from semantic discovery and automatic orchestration.

Orchestration of Semantic Web Services for Large-Scale Document Annotation