Enormous amount of semantic data is still being encoded in HTML documents. Identifying and annotating the semantic concepts implicit in such documents makes them directly amenable for Semantic Web processing. In this paper we describe a highly automated technique for annotating HTML documents, especially template-based content-rich documents, containing many different semantic concepts per document. Starting with a (small) seed of hand-labeled instances of semantic concepts in a set of HTML documents we bootstrap an annotation process that automatically identifies unlabeled concept instances present in other documents. The bootstrapping technique exploits the observation that semantically related items in content-rich documents exhibit consistency in presentation style and spatial locality to learn a statistical model for accurately identifying different semantic concepts in HTML documents drawn from a variety ofWeb sources. We also present experimental results on the effectiveness of the technique.
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