- From: Jodi Schneider <jodi.schneider@deri.org>
- Date: Fri, 3 Sep 2010 18:15:05 +0100
- To: HCLS IG <public-semweb-lifesci@w3.org>, Anita de Waard <A.dewaard@elsevier.com>, Sudeshna Das <sudeshna_das@harvard.edu>, Paolo Ciccarese <paolo.ciccarese@gmail.com>, David Shotton <david.shotton@zoo.ox.ac.uk>, Susanna-Assunta Sansone <sansone@ebi.ac.uk>, Carole Goble <carole.goble@manchester.ac.uk>, Gully Burns <gully@usc.edu>, Larry Hunter <Larry.Hunter@ucdenver.edu>, Larisa Soldatova <lss@aber.ac.uk>, James Evans <jevans@uchicago.edu>, Tim Clark <tim_clark@harvard.edu>, Tudor Groza <tudor.groza@deri.org>
Here's another interesting paper related to rhetorical structures: "Ontology-based Modelling of Related Work Sections in Research Articles: Using CRFs for Developing Semantic Data based Information Retrieval Systems" by Angrosh M.A., Stephen Cranefield and Nigel Stanger. Presented at iSemantics 2010. It uses machine learning to learn rhetorical structures and classify them at the sentence level into a "Sentence Context Ontology". Then the paper shows SPARQL queries to help answer queries like: 1. List current work outcomes and current work shortcomings of a research article 2. List the context of related work cited by author in the article 3. List the outcomes of related work mentioned by the author in the article 4. List related work which strongly support the article (related work strengths) 5. List the shortcomings of a specific cited work in an article 6. List alternative statements for a cited work (as opined by the author) 7. List contrasting work for a cited work 8. Identify the use of a cited work in different articles 9. Identify the context in which a cited work is used in different articles -Jodi PS-Labor Day is coming up. Do we meet again soon?
Received on Friday, 3 September 2010 17:15:39 UTC