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Provenance-aware document annotation

The goal of this subtask is to explore ways to share text mining results and image annotations through the AO (Annotation Ontology) model. We would like to collect feedback from the community to improve the representation, reach for consensus and check for interoperability results.

AO: Annotation Ontology

The scalability of the Semantic Web hinges upon factoring information components into orthogonal ontologies. In addition to limiting the combinatorial explosion implicit in multifunction ontologies, it allows people to share code and understanding of core ontologies. Annotation Ontology (AO) is a vocabulary designed to extensively reuse existing domain ontologies (entities annotations or semantic tags) and to provide several other kind of annotations - comments, textual annotation (classic tags), notes, examples, erratum... - on potentially any kind of document (text, images, audio...) and document fragments.

Use Cases

Manual Annotations

  1. ConceptWiki annotations, database, contents managed by Christine Chichester, HCLS contact: Marco Roos

Text Mining Results

  1. Information Enhancement and Improved Search of Biomedical Publications
  2. Is course-grained rhetorical blocks improving entity annotation tools performance?
  3. Annotation provenance in Collaborative Annotation of Large Biomedical Corpora (CALBC) Silver Standard Corpus
  4. Adrien Coulet's results of text mining using PharmGKB lexicon and NCBO services

Image Annotations

  1. ...

Open Discussions



Conference phone number

  • Dial-In #: +1.617.761.6200 (Cambridge, MA)
  • Dial-In #: + (Nice, France)
  • Dial-In #: +44.117.370.6152 (Bristol, UK)
  • Participant Access Code: 42572 ("HCLS2")
  • IRC Channel: irc.w3.org port 6665 channel #HCLS2 (see W3C IRC page for details, or see Web IRC)