Material Detail

This video was recorded at REASE. This is a one-hour video recording of the presentation of Fabio Ciravegna at the KnowledgeWeb summer school 2006. It comprises either the video synchronized with the slides (requires Flash) or the video alone. Table of Contents: Automating Document Annotation using HLT and ML Tutorial Outline Information searching Some hard facts Sources of Knowledge Traditional Approaches Factors hampering searching General Requirements General requirements: Multi-Mediality Jet engine example Requirements for Large Scale KM Requirements (ctd) Ontology-based Document Annotation Ontology-based Annotation When/What do we annotate? Ontology-based Annotation AktiveMedia: Annotation for text and images and across Text is selected and dropped into a concept in the ontology Contextual Annotation of Images and Text Annotating across documents (CREAM, 2001) Issues in User Centred Document Annotation Annotations: Where From? Manual Annotation (1) An Example Why not including Problems in the example Problems with Manual Annotation (2) Annotation for use... Doable? Manual Annotation (2) Automating Annotation for the Semantic Web Annotation Engines Advantages Using IE to support annotation: step 1 Using IE to support annotation: step 2 Learning curve Impact on Annotation Large Scale Annotation Armadillo Annotation as Harvesting Large Scale Extraction Strategy Information Extraction From Text Named Entity Recognition Traditional approach to NER&C Large Scale NER&C Large Scale NER: Indexing Known Name Recognition Discovery of New Names More complex IE: event modelling An Example of Automatic IE Information extraction Use of Annotated Material: Searching Querying the documents Querying documents Accessing Services Statistics in IPAS Information Integration Use of Integrated Information Gourm-adillo Martin Dzbor, John B. Domingue, and Enrico Motta. Magpie: - towards a semantic web browser. ISWC 20 Sources SimMetrics Another Type of Annotation: Braindump A different type of annotation: braindump Conclusions Future Work & Challenges A list of tools for automatic annotation

Disciplines:

• Science and Technology  / Computer Science