How Semantic Technology Helps Researchers


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This presentation provides three cases studies as to how the Collexis technology helps a researcher to be more effective in their researcher.

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  • STM Facts Over the last 12 years, the STM industry has loaded up 90% of the 23,000 journal titles. These titles generate in access of 800,000 articles per year for an estimated author community of 5.5 million worldwide researchers. It is estimated that it takes an author 90 – 100 hours to prepare a scientific article and it will take 2 – 3 reviewers 3 – 6 hours to conduct their peer review of a single article. Considering the time it takes the author to write their scientific article, consider the daunting task of the researcher to stay up on the ever growing number of scientific articles., their time is seriously being challenged. Mark Ware’s 2006 paper on the STM industry reported that size of single journal grew from 83 to 154 articles. The length of the average article grew from 7.4 to 12.4 pages and the total pages of the journal grew to 2,216 from 820 pages. A whopping 270%. Considering these statistics are a few years old and the trend is increasing each year, we know that the researcher’s burden becomes more substantial each year. Just as challenging is the academic library’s challenge to manage their collection within their budget. Unfortunately the average publisher journal price increase is always higher than the average library’s budget for serials and monographs. While the publishing community have brought great value to the research community by providing backfiles at a very reasonable cost and providing them access to their entire library of titles. The fact remains that the library’s budget and the publisher’s subscription price increases have been and will remain in conflict with each other.  
  • How Semantic Technology Helps Researchers

    1. 1. Darrell W. Gunter Collexis an Elsevier Company June 24, 2010 "How semantic technology enhances the productivity of scientific researchers."
    2. 2. Our Agenda For Today <ul><li>The Challenges of Scientific Research </li></ul><ul><li>The Collexis Technology </li></ul><ul><li>Case Studies </li></ul><ul><ul><li>Professional Networks </li></ul></ul><ul><ul><li>Institutional Networks Johns Hopkins & Asklepios </li></ul></ul><ul><ul><li>Managing the Peer Review Process </li></ul></ul><ul><li>Summation </li></ul>
    3. 3. The Facts 1,2,3 The Author is under great pressure!
    4. 4. Collexis Fingerprint Engine Collexis Technology KnowledgeBase Text Fingerprint
    5. 5. Collexis Knowledge Engine 7.0 Tokenizer Normalizer Abbreviation expansion Dehyphenation Language detection Coordination expansion Part-of-Speech tagging Entity recognition based on regular expressions Noun phrase detection Concept finding Part-of-speech based disambiguation of thesaurus concepts Exclude known idioms Fingerprint aggregation
    6. 6. Collexis Knowledge Engine 7.0 <ul><li>Modular NLP workbench – processing and analyzing of text documents </li></ul><ul><li>Retrieval and aggregation engine – serving the application layer </li></ul>
    7. 7. Collexis – selected references American Institute of Physics Stanford University Asklepios Kliniken Johnson & Johnson Johns Hopkins University University of California, San Franciscio Dana Farber Cancer Institute Harvard University National Institutes of Health Mayo Clinic California Institute for Quantitative Biosciences (QB3), The Wellcome Trust Albert Einstein College of Medicine
    8. 8. Explore instead of Searching!
    9. 9. Creating expert profiles from documents using semantic technologies <ul><li>Document fingerprints aggregated to expert profiles! </li></ul>
    10. 10. BiomedExperts – more than 300,000+ registered users <ul><li>Prepopulated network – based on PubMed </li></ul><ul><li>1.8 million precalculated experts </li></ul><ul><li>More than 24 million co-author relations between them </li></ul><ul><li>Representing over 3,500 institutions </li></ul><ul><li>From 190 countries </li></ul><ul><li>Growing each day between 500 and 1000 users </li></ul><ul><li>BME data used in other applications </li></ul>
    11. 12. <ul><li>Co-author based networks </li></ul>
    12. 13. <ul><li>Geographical </li></ul><ul><li>mapping of the </li></ul><ul><li>co-author network </li></ul>
    13. 16. Johns Hopkins: The Issue! Connecting Experts <ul><li>Fall retreat: Main issue how do they take advantage of the university’s expertise and build collaboration </li></ul><ul><li>First solution – Repurpose a parking lot to be a coffee shop for the JH community to grab a cup of Joe and find new collaborators. </li></ul><ul><li>Outcome – Great coffee, great conversation but collaboration did not take off. </li></ul><ul><li>The Collexis Solution – Expert Institutional Dashboard! </li></ul>
    14. 21. Same Application for the NIH
    15. 22. Asklepios Facts and Figures <ul><li>Asklepios - Europe‘s largest health care provider </li></ul><ul><ul><li>500.000 patients for inpatient care per year, 95 hospitals, 21.000 beds </li></ul></ul><ul><ul><li>34.500 employees </li></ul></ul><ul><ul><li>Asklepios owns medical nursing and allied health schools </li></ul></ul><ul><ul><li>Home care programs and residential care programs </li></ul></ul><ul><li>Asklepios International </li></ul><ul><ul><li>Pacific Health System – California </li></ul></ul><ul><ul><li>Greece, Athens Medical Center </li></ul></ul><ul><ul><li>University hospital in Shanghai: Joint Venture with Siemens and Tongji University </li></ul></ul>
    16. 23. Optimize Workflows (e.g. Avoid interruptions caused by knowledge search, retrieval, and application) Distribute Expert Knowledge (across multiple locations, time zones, medical conditions) Guide Workflows (e.g. Care Plans, Expert-Task Context Allocation) Help Asklepios to know “what Asklepios knows” Stimulate new Knowledge Acquisition Usage Models Why Knowledge Management?
    17. 24. <ul><li>Patient, male, age of 62, needs a knee joint prosthesis due to Rheumatoid Arthritis </li></ul><ul><li>Where is the best place to get it? </li></ul><ul><li>Criteria which will be taken into account: </li></ul><ul><ul><li>Geographical aspects </li></ul></ul><ul><ul><li>Recommendation of his GP </li></ul></ul><ul><ul><li>Publicly available information - mostly via Internet </li></ul></ul><ul><li>Strongest competitors: university hospitals (within the region) </li></ul>Use Case 1 – Expert profiles
    18. 25. Asklepios Research Profiles
    19. 26. Expert Profile of Prof. Grifka
    20. 27. Make Internal Expertise available!
    21. 28. Help Asklepios to know “what Asklepios knows” Virtualize expertise!
    22. 29. Provide a single point of search for all relevant content from publishers! Link internal expertise / experience and external knowledge sources!
    23. 30. <ul><li>Patient with lung cancer and reduced renal function </li></ul><ul><li>Decision in chemotherapeutic drug is pending </li></ul><ul><li>Preferred choice: Cisplatin as chemotherapeutic agent </li></ul><ul><li>Open questions: can Cisplatin be used which has nephrotoxicity as a side effect? </li></ul>Use Case 4 - External Resources and Internal Experience Use Case 4 - External Resources and Internal Experience
    24. 31. Search - Cisplatin shows the relevant publications from Springer, Elsevier, Thieme, OVID and other publishers Asklepios Intelligent Digital Library
    25. 32. <ul><li>Opening an journal article… </li></ul><ul><li>… shows immediately similar publications colleagues </li></ul><ul><ul><ul><ul><li> … and the names and expert profiles </li></ul></ul></ul></ul>Link External Knowledge and Internal Expertise
    26. 33. Key Issues in STM Industry <ul><li>Publishers / Editors </li></ul><ul><ul><li>Finding the right reviewer </li></ul></ul><ul><ul><li>Expanding their pool of reviewers </li></ul></ul><ul><li>Institutions </li></ul><ul><ul><li>Determining what grants they should go after </li></ul></ul><ul><ul><li>Determining who within their organization is best to apply </li></ul></ul><ul><li>Grant Funding Organizations </li></ul><ul><ul><li>Analyzing the vast amount of grant applications submitted. </li></ul></ul><ul><ul><li>Determining who within the organization is best qualified to review the grant application (known and unknown) </li></ul></ul>
    27. 34. The Challenge for STM Publishers <ul><li>Receive thousands of manuscripts annually </li></ul><ul><li>Timely process to conduct the Peer Review Process </li></ul><ul><li>Timely process to determine who should review it. </li></ul><ul><li>Important for reviewer to free of conflicts of interest </li></ul><ul><li>Ethics of review process are paramount </li></ul>
    28. 39. Key Benefits Reviewer Finder <ul><li>Fingerprint of manuscript - Clarity </li></ul><ul><li>Determine the best reviewer </li></ul><ul><li>Free of conflicts </li></ul><ul><li>More efficient and effective process </li></ul><ul><li>Ultimately increases profitability </li></ul>
    29. 40. The effectiveness of Semantic Technology <ul><li>Aggregates the researcher's publications into a Fingerprint of weighted relevant concepts </li></ul><ul><li>Expert Profiles (individual, institution, dept, country,etc.) </li></ul><ul><li>Shows co-author relationships (who publishes with whom) </li></ul><ul><li>Conduct search by key concepts </li></ul><ul><li>Match content from a variety of sources based on a key concept, researcher, country, etc. </li></ul><ul><li>Determine expert for peer review, grant application, project, etc. </li></ul>
    30. 41. Thank you for your attention! <ul><li>Darrell W. Gunter </li></ul><ul><li>[email_address] , cell +1-973-454-3475 </li></ul>