The Fast and the Numerous — Combining Machine and Community Intelligence for Semantic Annotation

Contents

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 required to semantically enrich Web content at a large scale. 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. In this paper, we describe the requirements on our model, its concrete implementation based on Semantic MediaWiki and an information extraction system and discuss lessons learned from practical experience with real users. These experiences allow us to conclude that our model is a promising approach towards leveraging semantic annotation.