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Automated Semantic Analysis of Schematic Data - Learning-based Techniques for Scalable and Automated Semantic Understanding of Template Generated Sche
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Content in numerous data sources are not directly amenable tomachine processing. This book describes techniques for automatedsemantic analysis of schematic content which are characterized bybeing populated from backend databases. Starting with a seed set ofhand-labeled instances of semantic concepts in a set of HTMLdocuments, a technique is devised that bootstraps an annotationprocess for automatic identification of concept instances presentin other documents. The technique exploits the observation thatsemantically related items in schematic HTML documents exhibitconsistency in presentation style and spatial locality to learnstatistical concept models, using light-weight semantic features.This model directs the annotation of diverse Web documentspossessing similar content semantics. The power of these techniquesis demonstrated through applications developed for real-lifeproblems that include audio-basedassistive browsing for non-visualWeb access, focused browsing on handhelds with semantic bookmarks,text data cleaning, and accurate identification of remote homologsof biological protein sequences.
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