Content Mining of Science in Cambridge
Download as PPTX, PDF1 like184 views
The document discusses the concept and utility of content mining in scientific literature, focusing on the efforts of Cambridge University to become a leader in this field. It outlines various applications, such as mapping clinical trials and extracting chemical reactions, while addressing challenges posed by publishers regarding mining legality and access. Contentmine is presented as a comprehensive platform for efficiently mining and analyzing scientific data from a large volume of publications.
![STM Publishers prevent Mining
• FUD & disinformation about legality (Elsevier)
• Monopolies on infrastructure (“API”s, CCC
Rightfind)
• Technical obstruction (Wiley Captcha,
Macmillan Readcube)
• Restrictive contracts with libraries (ALL) [1]
• Wasting my/our time (ALL)
[1] [You may not] utilize the TDM Output to enhance … subject repositories
in a way that would [… ] have the potential to substitute and/or replicate
any other existing Elsevier products, services and/or solutions.](https://image.slidesharecdn.com/camlib-160108093231-160112171009/85/Content-Mining-of-Science-in-Cambridge-20-320.jpg)
![WILEY … “new security feature… to prevent systematic download of content
“[limit of] 100 papers per day”
“essential security feature … to protect both parties (sic)”
CAPTCHA
User has to type words](https://image.slidesharecdn.com/camlib-160108093231-160112171009/85/Content-Mining-of-Science-in-Cambridge-21-320.jpg)
Content Mining of Science in Cambridge
- 1. Content Mining of Science in Cambridge Peter Murray-Rust, Dept of Chemistry, University of Cambridge libraries@cambridge, Cambridge, UK 2016-01-07 What is mining? Why is it useful? Open Access and UK “Hargreaves” legislation How Cambridge can become a world leader
- 2. The Right to Read is the Right to Mine**PeterMurray-Rust, 2011 http://contentmine.org
- 3. Use Cases of ContentMining • Epidemiology of obesity (Cambridge U) • (OKF, OpenTrials) Mapping clinical trials repositories to reports in scientific literature • Mining chemical reactions from patents • Creating a bacterial supertree-of-life from 4500 papers
- 4. Polly has 20 seconds to read this paper… …and 10,000 more
- 5. ContentMine software can do this in a few minutes Polly: “there were 10,000 abstracts and due to time pressures, we split this between 6 researchers. It took about 2-3 days of work (working only on this) to get through ~1,600 papers each. So, at a minimum this equates to 12 days of full-time work (and would normally be done over several weeks under normal time pressures).”
- 6. 400,000 Clinical Trials In 10 government registries Mapping trials => papers http://www.trialsjournal.com/content/16/1/80 2009 => 2015. What’s happened in last 6 years?? Search the whole scientific literature For “2009-0100068-41”
- 7. ContentMine-ing strategy • Discover. Crawl the COMPLETE relevant literature. => bibliography • Scrape (download). ALL papers • Index papers => Facts • Search/analyze papers => complex science • Extract, Annotate, Aggregate (“Transformative”)
- 8. What is “Content”? http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.01113 03&representation=PDF CC-BY SECTIONS MAPS TABLES CHEMISTRY TEXT MATH contentmine.org tackles these
- 9. catalogue getpapers query Daily Crawl EuPMC, arXiv CORE , HAL, (UNIV repos) ToC services PDF HTML DOC ePUB TeX XML PNG EPS CSV XLSURLs DOIs crawl quickscrape norma Normalizer Structurer Semantic Tagger Text Data Figures ami UNIV Repos search Lookup CONTENT MINING Chem Phylo Trials Crystal Plants COMMUNITY plugins Visualization and Analysis PloSONE, BMC, peerJ… Nature, IEEE, Elsevier… Publisher Sites scrapers queries taggers abstract methods references Captioned Figures Fig. 1 HTML tables 30, 000 pages/day Semantic ScholarlyHTML Facts CONTENTMINE Complete OPEN Platform for Mining Scientific Literature
- 11. 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.
- 12. Facts in context daily IUCN endangered species news en.wikipedia.org CC By-SA
- 13. ContentMine Fact of The Day • Fact of the day • Endangered species in recent science • Facts • Bubbles
- 15. “Root” 4500 papers each with 1 tree
- 16. OCR (Tesseract) Norma (imageanalysis) (((((Pyramidobacter_piscolens:195,Jonquetella_anthropi:135):86,Synergistes_jonesii:301):131,Thermotoga _maritime:357):12,(Mycobacterium_tuberculosis:223,Bifidobacterium_longum:333):158):10,((Optiutus_te rrae:441,(((Borrelia_burgdorferi:…202):91):22):32,(Proprinogenum_modestus:124,Fusobacterium_nucleat um:167):217):11):9); Semantic re-usable/computable output (ca 4 secs/image)
- 17. Supertree for 924 species Tree
- 18. Supertree created from 4300 papers
- 19. Copyright and Mining • UK (“Hargreaves”) 2014 legislation: – “personal” “non-commercial*” “research” “data analytics” – legitimizes copying (?to disk), but not publishing *teaching, textbooks, etc. may be “commercial”
- 20. STM Publishers prevent Mining • FUD & disinformation about legality (Elsevier) • Monopolies on infrastructure (“API”s, CCC Rightfind) • Technical obstruction (Wiley Captcha, Macmillan Readcube) • Restrictive contracts with libraries (ALL) [1] • Wasting my/our time (ALL) [1] [You may not] utilize the TDM Output to enhance … subject repositories in a way that would [… ] have the potential to substitute and/or replicate any other existing Elsevier products, services and/or solutions.
- 21. WILEY … “new security feature… to prevent systematic download of content “[limit of] 100 papers per day” “essential security feature … to protect both parties (sic)” CAPTCHA User has to type words
- 22. ContentMine working with Libraries • Cambridge: Library, Plant Sciences, Public Health, Chemistry • Cochrane Collaboration on Systematic Reviews of Clinical Trials • FutureTDM (H2020, LIBER) • Running workshops and training • We have dedicated servers running in chemistry
- 23. My European Heroes Young People(ContentMine) NEELIE KROES
Editor's Notes
- #2: Hi, I’m here to talk about AMI; a data extraction framework and tool. First, I just want highlight some of key contributors to the projects; Andy for his work on the ChemistryVisitor and Peter for the overall architecture. In this talk, I’m going to impress the importance of data in a specific format and its utility to automated machine processing. Then I’m going to demonstrate AMI’s architecture and the transformation of data as it flows through the process. I’m going to dwell a little on a core format used, Scalable Vector Graphics (SVG) before introducing the concept of visitors, which are pluggable context specific data extractors. Next, I’m going to introduce Andy’s ChemVisitor, for extracting semantic chemistry data, along with a few other visitors that can process non-chemistry specific data. Finally, I will demonstrate some uses of the ChemVisitor, within the realm of validation and metabolism.