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Content Mining a short introduction to practices and policies

Content Mining a short introduction to practices and policies. Summary of a study for the Publishing Research Consortium into Journal Article Mining, By Eefke Smit, Maurits van der Graaf (2011) Full study available on PRC website. Let ’ s start with a potential user (1). Use-case-1:

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Content Mining a short introduction to practices and policies

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  1. Content Mininga short introduction to practices and policies Summary of a study for the Publishing Research Consortium into Journal Article Mining, By Eefke Smit, Maurits van der Graaf (2011) Full study available on PRC website

  2. Let’s start with a potential user (1) Use-case-1: keeping up-to-date Prof. Joost Verhaagen PhD, Netherlands Institute for Neuroscience, Amsterdam • Since 1982: • 90,000 journal articles on neuroregeneration (e.g. spinal cord injury) • New articles: • on average 22 journal articles per day on neuroregeneration

  3. Let’s start with a potential user (2) Use-case-2: Information needed as result of laboratory experiments Prof. Joost Verhaagen PhD, Netherlands Institute for Neuroscience, Amsterdam • Which molecules do play a role in this process? • Typical outcome of an experiment: hundreds of molecules show enhanced activity • Next step: how to filter out the relevant molecules? • ‘You would like to have for each of these molecules a meta-analysis about what is already known about these molecules in other processes’

  4. The essence of TDM is: So much information to analyze: Can a machine do this for him ?

  5. What TDM looks like: Textmining tool for semantic search byPubTator, seehttp://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/tutorial/index.html

  6. Typical text mining consists of • Processing large corpora of text in an automated way • To identify entities, instances, actions, relationships and patterns and do further analysis (e.g. on assertions or sentiments) • Examples: genes, proteins, gene-disease patterns, compound properties, chemical structures, side effects of drugs • Text mining output typically consists of a new metadata layer for the information: • Article clusters and categorisations, indexes • Topical maps, to show the occurrence of topics and their inter-relationships • Databases with facts, patterns, relationships, statements, assertions, properties found in the articles, • Visualisations like graphs, mappings, plot-graphs and topical maps

  7. Study commisioned in 2011 bythe Publishing Research Consortium • Authors: • Eefke Smit, Intn’l Assoc of STM Publishers • Maurits van der Graaf, Pleiade Management & Consultancy • Two parts: • Qualitative study: • 29 interviews with experts in academia, research, libraries, vendors and publishers • Quantitative study • Survey among publishers (members Crossref & STM) • 190 responses • Full report on PRC website www.publishingresearch.net • Article in the 1st issue of 2012 of Learned Publishing

  8. Optimists and Pessimists on TDM Optimistsabout TDM: • Vast digital corpus available and growing • More and more application areas (business, legal, social, etc) • Tools improvingfast • Manualworkreduced • Public domain or domain precision • Processing power less of a problem, analytical tools better, visualisationadds to analysis Skepticsabout TDM: • Has alwaysover-promised • Only in specializedfields • Tools stillcomplicated • Manualcurationnecessary • High investments • Domain dependent • No commondictionary • Overambition in the promise of knowledgediscovery

  9. Publishers are optimistic:Opinions/ expectations for Content Mining in the next 3 years

  10. Publishers are optimistic, continued:Opinions/ expectations for Content Mining on scholarly content in the next 3 years

  11. …but publishers do not yet get many mining requests from 3rd parties:

  12. Publishers are liberal in allowing mining:How case-by-case requests are treated

  13. …and plan more mining themselves:for retrieval and navigation

  14. Cross-sector solutions to facilitate Content Mining better Suggestions made by experts during the interviews: • Standardization of Content Formats • One Content Mining platform • Commonly agreed access and permission terms • One window for mining permissions • Collaboration with national libraries (ad 3: most interviewed experts do NOT see Open Access as a related issue; access terms also relate to datafile delivery or mining on the platform itself)

  15. Survey results for the 5 suggestions for cross-sector solutions

  16. Standardisation best prefered,of content formats and of API’s Top 3 for all Respondents: • Standardisation of Formats • One Mining Platform • Agreed Permission Terms Top 3 for Experts only: • Standardisation of Formats • Agreed Permission Terms • One Mining Platform Experts believe less in one platform and support standardisation even stronger, not just for content, also for APIs:

  17. Progress since 2011 on collective solutions facilitating TDM: • Commonly agreed permission terms: • STM standard clause for TDM on subscribed content for non commercial TDM • STM standard clause for pharma-TDM via Pharma-Documentention-Ring (PDR) • One window for mining permissions across publishers: • PLS/ CLA prototype, sepcially serving small and medium sized publishers • Obtain minable content in one place and provide standardized APIs and content formats: • CCC content mining platform • Crossref project-Prospect: single point web access for multiple publishers

  18. Questions ? Eefke Smit Director Standards and Technology International Association of STM Publishers smit@stm-assoc.org

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