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5-minute introduction for a workshop at OpenCon2014, Washington, exploring research projects for ContentMining

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  1. 1. Content Mining Ross Mounce and Peter Murray-Rust OpenCon, Washington, 2014-11-15
  2. 2. G***** for Science If you had a magic wand what would you want to be able to search for in the current “paper” literature[1]? Anything “readable”, including text, equations, diagrams, metadata, supplementary data. [1] Including closed access.
  3. 3. The Right to Read is the Right to Mine
  4. 4. • Crawl scientific literature (Open Bibliography) • Scrape each scientific article (ContentMine-quickscrape) • Extract the facts (ContentMine-AMI) • Index (Wikipedia) • Republish (WikiData) Machine Extraction of scientific facts
  5. 5. RSU: Richard Smith-Unna PMR: Peter Murray-Rust CL: CottageLabs Queues Repos Scientific literature Science Plugins Science Volunteers
  6. 6. Facts Marked by “non-scientists” in ContentMine workshops With Wikipedia everyone can be a scientist
  7. 7. “nuggets” in a scientific paper quantity units Value ranges Humans aren’t designed to mine this …  chemical project places
  8. 8. • Typical Typical chemical synthesis
  9. 9. Open Content Mining of FACTs Machines can interpret chemical reactions We have done 500,000 patents. There are > 3,000,000 reactions/year. Added value > 1B Eur.
  10. 10. AMI Example reaction scheme, taken from MDPI Metabolites 2012, 2, 100-133; page 8, CC-BY: AMI reads the complete diagram, recognizes the paths and generates the molecules. Then she creates a stop-fram animation showing how the 12 reactions lead into each other CLICK HERE FOR ANIMATION (may be browser dependent)
  11. 11. But we can now turn PDFs into Science We can’t turn a hamburger into a cow
  12. 12. Bacterial WP_phylogenetic tree Our machines have read and interpreted 4300 in an hour with > 95% accuracy Trees From used under new UK legislation (Hargreaves) WP: Clostridium_butyricum Genbank ID American Type Culture Collection
  13. 13. ( – “Adaptive Evolution of HIV at HLA Epitopes Is Associated with Ethnicity in Canada” . ((n122,((n121,n205),((n39,(n84,((((n35,n98),n191),n22),n17))),((n10,n182),( (((n232,n76),n68),(n109,n30)),(n73,(n106,n58))))))),((((((n103,n86),(n218,(n 215,n157))),((n164,n143),((n190,((n108,n177),(n192,n220))),((n233,n187), n41)))),((((n59,n184),((n134,n200),(n137,(n212,((n92,n209),n29))))),(n88,(n 102,n161))),((((n70,n140),(n18,n188)),(n49,((n123,n132),(n219,n198)))),((( n37,(n65,n46)),(n135,(n11,(n113,n142)))),(n210,((n69,(n216,n36)),(n231,n1 60))))))),(((n107,n43),((n149,n199),n74)),(((n101,(n19,n54)),n96),(n7,((n139 ,n5),((n170,(n25,n75)),(n146,(n154,(n194,(((n14,n116),n112),(n126,n222))) )))))))),(((((n165,(n168,n128)),n129),((n114,n181),(n48,n118))),((n158,(n91,( n33,n213))),(n87,n235))),((n197,(n175,n117)),(n196,((n171,(n163,n227)),(( n53,n131),n159)))))));
  14. 14. Content Mining is now legal in UK • Since 2014-06-01 • For non-commercial research purposes • Without permission or licence from publishers Come to the UK!
  15. 15. Some Community Ideas • Exoplanets • Elementary particles. • NASA missions • Biodiversity (what lives where and when) • Phytochemistry (chemistry in plants) • Neurophysiology data (electrical spikes) • Evolutionary biology • Daily dinosaurs for 5-year-olds • Legal and political aspects • Licence and Funder metadata