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Textmining activities at BioHackathon 2010
The document outlines the goals and achievements of a team participating in the 2010 Biohackathon focused on semantic text mining. Key tasks included integrating text mining results across various platforms and exploring relationships within biomedical data. The initiatives involved the use of natural language processing and APIs like Medie and Enju for data retrieval and parsing.
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Textmining activities at BioHackathon 2010
- 1. Semantic Textmining Goalsand achievements BioHackathon 2010
- 3. Team members • Hammad • Matthias • Venkata • Heiko • YAMAMOTO-san • Alberto
- 4. Original Proposal • Integrationof 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
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- 8. RDF schema forTM <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>
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- 10. MEDIE and EnjuAPIs • 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.
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- 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
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