1. The Role of Ontologies in Improved Scholarly Communication Philip E. Bourne University of California San Diego pbourne@ucsd.edu http://www.sdsc.edu/pb

2. My Perspective … Ontology Developer (years ago – mmCIF - Bioinformatics 2002 18: 1280-128) Database Developer – RCSB PDB Supporter of open access (provided there is a business model) - editor in chief of PLoS Computational Biology Co-founder - SciVee Inc. I am becoming increasingly interested in scholarly communication I use ontologies to support this work

3. Objective Today Describe how we are using ontologies to try and improve scholarly communication Motivate you towards thinking about ontologies that should be developed Learn from you where we might spend our efforts

4. First Consider What Motivates Us to Improve Scholarly Communication

5. We Cannot Possibly Read a Fraction of the Papers We Should Drivers of ChangeRenear & Palmer 2009 Science 325:828-832

6. Hence We Are Scanning More Reading Less Renear & Palmer 2009 Science 325:828-832Drivers of Change

7. The Truth About the Scientific eLaboratory I have ?? mail folders! The intellectual memory of my laboratory is in those folders This is an unhealthy hub and spoke mentality Drivers of Change

8. The Truth About the Scientific eLaboratory I generate way more negative that positive data, but where is it? Content management is a mess – Slides, posters….. – Data, lab notebooks …. – Collaborations, Journal clubs … Software is open but where is it? Farewell is for the data too Drivers of Change Computational Biology Resources Lack Persistence and Usability. PLoS Comp. Biol. 4(7): e1000136

9. Data and the Publication Are Disjoint PubMed contains 18,792,257 entries ~100,000 papers indexed per month In Feb 2009: – 67,406,898 interactive searches were done – 92,216,786 entries were viewed 1078 databases reported in NAR 2008 MetaBase http://biodatabase.org reports 2,651 entries edited 12,587 times http://biodatabase.org Biosciences Data as of April 14, 2009Drivers of Change

10. Publishing Limitations A paper is an artifact of a previous era It is not the logical end product of eScience, hence: – Work is omitted – Article vs supplement is a mess – Visualization may be limited – Interaction and enquiry are non-existent – Rich media can help, but are rarely used Drivers of Change

11. We Need to do Better & The Game is Afoot It is being driven from the top down and the bottom up

12. Ontologies & Semantic Tagging

13. BioLit Data Extraction/Storage Database IDs Ontology terms Text excerpts Other… BioLit MySQL database XML XML, Meta-data web external databases Semantic Tagging

14. Tagging of PubMed Central Ontologies read from OBO Files Words converted to tree structures Matched to every non-trivial word in the paper Matches tagged A long paper can be matched to GO in less than 30 seconds Semantic Tagginghttp://biolit.ucsd.edu

15. Semantic Tagginghttp://biolit.ucsd.edu

16. ICTP Trieste, December 10, 2007 16 http://biolit.ucsd.eduSemantic Tagging

17. Provision of Webservices to this tagging may be the most valuable contribution.. Semantic Tagging

18. www.rcsb.org/pdb/explore/literature.do?structureId=1TIM Database & Literature Integration Context BMC Bioinformatics 2010 11:220Semantic Tagging

19. Semantic Tagging of Database Content http://www.pdb.org PLoS Comp. Biol. 6(2) e1000673 Semantic Tagging

20. Automatic Knowledge Discovery for Those with No Time to Read Immunology Literature Cardiac Disease Literature Shared Function Semantic Tagging

21. This is Literature Post-processing Better to Get the Authors Involved Authors are the absolute experts on the content More effective distribution of labor Add metadata before the article enters the publishing process BMC Bioinformatics 2010 11:103 Semantic Tagging

22. Word 2007 Add-in for Authors Allows authors to add metadata as they write, before they submit the manuscript Authors are assisted by automated term recognition – OBO ontologies – Database IDs Metadata are embedded directly into the manuscript document via XML tags, OOXML format – Open – Machine-readable Open source, Microsoft Public License http://www.codeplex.com/ucsdbiolitDrivers of Change

23. Word 2007 Add-in Example of What it Looks Like - Ontologies Inline Recognition, Highlighting, and Mark-up of Informative Terms – A recognized term will have a dotted, purple underline – Hovering generates a Smart Tag above the term add mark-up for this term ignore this term view the term in the ontology browser If a recognized term appears in more than one ontology, all instances of that term will be listed – Hovering over a marked-up term option to apply mark-up to all recognized instances of term stop recognizing a term – Pass ontology terms back to provider Semantic Tagging BMC Bioinformatics 2010 11:103

24. Built-in Knowledge of Ontologies and Databases – Add-in provides a list of biomedical ontologies to download – and a list of databases for ID recognition (GenBank/RefSeq, UniProt, Protein Data Bank) – A user may also supply a URL to download other ontologies Ontology Browser – allows a user to select an ontology and then navigate through it to view terms and their relationships BMC Bioinformatics 2010 11:103

25. Custom Metadata Ontologies do not contain all usages of a concept Add-in allows user to assign custom metadata Human Disease Ontology term: Leukemia, T-Cell, HTLV- II-Associated Synonym: Atypical hairy cell leukemia (disorder) Actual use in literature: – hairy cell leukemia – hairy-cell leukemia – hairy T cell leukemia – T cell hairy leukemia BMC Bioinformatics 2010 11:103

26. Synonym mapping, disambiguation Inclusion of an additional set of synonyms for a term that reflect its use in natural language – Automated finding of synonyms in extant literature – Gather synonyms from term-mapping databases Incorporate a more sophisticated term recognition approach into the add-in BMC Bioinformatics 2010 11:103

27. Challenges Author use – Familiarity with ontologies, terms – Agreement between co-authors End-use of semantically enriched manuscript Need to combine with NLM XML standard Semantic Tagging BMC Bioinformatics 2010 11:103

28. Challenges: Author Use IF one or more publishers fast tracked a paper that had semantic markup I would argue it would catch on in no time Semantic Tagging BMC Bioinformatics 2010 11:103

29. Where we Need {Better} Ontologies 1. To Support Mashups Between Different Types of Scholarly Output

30. Post-publication of Video and Paper www.scivee.tv Drivers of Change

31. Pubcast – Video Integrated with the Full Text of the Paper

32. Pubcasts - A Unique Technology Don’t understand what you are reading? Click and have the author pop-up and explain it! See the scientists and the experiments behind the research papers and textbooks Pubcasts - A Blend of Video, text, tables, figures, PowerPoints, comments, ratings… ALL SYNCHRONIZED FOR RAPID LEARNING Mashups – www.scivee.tv

33. Where we Need {Better} Ontologies 2. To Support Tagging of all Aspects of the Scholarly Product

34. Consider Today’s Academic Workflow Research [Grants] Journal Article Conference Paper Poster Session Feds Societies Publishers Reviews Blogs Community Service/Data Curation What Should be Done?

35. Consider Tomorrow’s Academic Workflow Research [Grants] Journal Article Conference Paper Poster Session Feds Societies Publishers Reviews Blogs Community Service/Data Curation Ideas, Data, Hypotheses What Should be Done?

36. Maybe The Line is Somewhere Else? Scientist Idea Experiment Data Conclusions Publish Laboratory Publisher

37. Maybe The Line is Somewhere Else? Scientist Idea Experiment Data Conclusions Publish What Should We Do? Laboratory Publisher Institution Lab Notebook

38. Crowd Sourcing the Electronic Printing Press (aka Workshop: Beyond the PDF) Proposal to the US National Science Foundation: Aims: – Define user requirements – Establish a specification document – Open source the development effort – Have a commitment from a publisher to publish a research object using the system – Act as an exemplar for what can be done

39. Question: What if Everyone Had An Electronic Printing Press? Peer review might change? Bibliometrics might change? Business models will likely change? What happens to the database/literature divide? Societies might do more self publishing? We might have improved the dissemination of science, but will we have improved the comprehension?

40. General References What Do I Want from the Publisher of the Future PLoS Comp Biol http://www.sdsc.edu/pb http://www.sdsc.edu/pb Fourth Paradigm: Data Intensive Scientific Discovery http://research.microsoft.com/enus/collabora tion/fourthparadigm/

41. References to Exemplars Semantic Biochemical Journal - 2010: Using Utopia Article of the Future, Cell, 2009: Prospect, Royal Society of Chemistry, 2009: Adventures in Semantic Publishing, Oxford U, 2009: The Structured Digital Abstract, Seringhaus/Gerstein, 2008

42. Acknowledgements BioLit Team – Lynn Fink – Parker Williams – Marco Martinez – Rahul Chandran – Greg Quinn Microsoft Scholarly Communications – Pablo Fernicola – Lee Dirks – Savas Parastitidas – Alex Wade – Tony Hey wwPDB team SciVee Team – Apryl Bailey – Tim Beck – Leo Chalupa – Lynn Fink – Marc Friedman (CEO) – Ken Liu – Alex Ramos – Willy Suwanto http://www.scivee.tv http://biolit.ucsd.edu http//www.pdb.org http://www.codeplex.com/ucsdbiolit

43. Questions? pbourne@ucsd.edu