The Fast and the Numerous
The Fast and the Numerous – Combining Machine and Community Intelligence for Semantic Annotation
Abstract. Starting from the observation that certain communities have incentive mechanisms in place to create large amounts of unstructured content, we propose in this paper an original model which we expect to lead to the large number of annotations the Semantic Web urgently needs. The novelty of our model lies in the combination of two key ingredients: the effort that online communities are making to create content and the capability of machines to detect regular patterns in user annotation to suggest new annotations. Provided that the creation of semantic content is made easy enough and incentives are in place, we can assume that these communities will be willing to provide annotations. However, as human resources are clearly limited, we aim at integrating algorithmic support into our model to bootstrap on existing annotations and learn patterns to be used for suggesting new annotations. As the automatically extracted information needs to be validated, our model presents the extracted knowledge to the user in the form of questions, thus allowing for the validation of the information.
Published at WIKIAI-08 (Workshop paper)
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- Sebastian Blohm, Markus Krötzsch, Philipp Cimiano. The Fast and the Numerous – Combining Machine and Community Intelligence for Semantic Annotation. In Proceedings of the AAAI 2008 Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy (WIKIAI-08). 2008.