Summary of the activities developed by the text mining task force during the BioHackathon 2010 http://hackathon3.dbcls.jp/wiki/TextMining

1. Semantic Textmining
Goals and achievements
BioHackathon 2010

3. Team members
• Hammad
• Matthias
• Venkata
• Heiko
• YAMAMOTO-san
• Alberto

4. Original Proposal
• Integration of text mining results
– Reflect / Whatizit / Medie
– Results as triplets
• URI and predicates
– Implementation with SADI
– Result presentation using aTag
• Explore relations
• Interfaces

5. The work done
• Integration of text mining results
– Reflect / Whatizit / Medie
– Results as triplets
• URI and predicates
– Future BioPython module and REST service
• Explore relations
– Sesame endpoint
– Biogateway
– ARQ for federated queries
• Interfaces
– Result presentation using aTag
– Exhibit faceted interface

6. http://whatizit.neurocommons.org/

7. http://whatizit.neurocommons.org/

8. RDF schema for TM
<rdf:Description>
<rdf:type rdf:resource="http://rdfs.org/sioc/ns#Item"/>
<sioc:about rdf:resource="http://www.ncbi.nlm.nih.gov/pubmed/9002550"/>
<sioc:content>SBMA</sioc:content>
<sioc:topic rdf:resource="http://purl.uniprot.org/uniprot/P10275"/>
<rdfs:seeAlso rdf:resource="http://www.ncbi.nlm.nih.gov/pubmed/9002550"/>
</rdf:Description>

9. http://reflect.ws

10. MEDIE and Enju APIs
• MEDIE is an intelligent search
engine to retrieve biomedical
correlations from MEDLINE, based
on indexing by Natural Language
Processing and Text Mining
techniques.
• Enju is a syntactic parser for
English.

11. Medie XML output
http://www-tsujii.is.s.u-tokyo.ac.jp/medie/dbcls.cgi?pmid=19116711

12. Enju XML output
http://docman.dbcls.jp/medieconv?pmid=17551671

13. http://togows.dbcls.jp/entry/pubmed/pmid.ttl
http://www.uniprot.org/uniprot/P12345.rdf

14. Workflow
Whatizit Reflect Medie Pubmed
XML XML XML
RDF
RDF TogoWS
RDF Uniprot

15. Substance A
Interacts with
Receptor B
Region C
axonal projections
brain region D
Region D
aversive stimuli
Interlink these entities with
taxonomies & ontologies

16. TMOntology

17. http://hackathon3.dbcls.jp/wiki/TextMining