1. Profiles Research Networking Software Users Group Meeting http://profiles.catalyst.harvard.edu http://profiles.catalyst.harvard.edu February 17, 2012

2. Agenda Welcome to new members Updates/news Profiles 1.0 Search

3. Profiles Users Group Members UCSF Fred Hutchinson CRC Oregon Health Sci U UC Davis (CBST) U Southern California UC San Diego Charles Drew U Hawaii Arizona State Montana State U Colorado Denver UW Madison U Illinois U Chicago Baylor College Med UT Southwestern UT Houston Jackson State (RTRN) Ohio State Cincinnati Children’s Case Western U Kentucky Vanderbilt Stem Cell Leadership in Med U Alabama Birmingham Harvard Univ Minnesota Dartmouth Univ Mass Boston Univ Tufts Univ Boston VA Rensselaer Univ Connecticut Univ Rochester NYU Med Ctr MedMeme Thomas Jefferson UPenn Johns Hopkins USUHS-CNRM NIH George Wash U Penn State Childrens Nat Med Ctr Wake Forest HSSC Georgia Tech Piedmont Healthcare Emory University McGill University (Canada) Ministério da Ciência e Tecnologia e Inovação (Brazil) University of Cambridge (UK) Clinical & Biomedical Computing Ltd (UK) Symplectic Limited (UK) Makerere University (Uganda) Velammal Engineering College (India) Nati Sci Lib, Chinese Acad of Sci (China) Beijing Normal University (China) University of the South Pacific (Fiji)

4. University Spotlights Harvard UniversityUCSF University of Minnesota South Carolina Penn State UConn Health Center http://connects.catalyst.harvard.edu/profileshttp://profiles.ucsf.edu http://profiles.ahc.umn.edu http://profiles.healthsciencessc.org http://profiles.psu.edu http://profiles.uconn.edu Wake Forest Medicne http://profiles.tsi.wakehealth.edu

5. Upcoming Conferences International Network for Social Network Analysis (INSNA) Sunbelt Conference, Redondo Beach, CA, March 12-18, 2012 American Medical Informatics Association (AMIA) Joint Summits on Translational Science, San Francisco, CA, March 19-23, 2012 CTSA Social Network Analysis Workshop, UC Davis, Sacramento, CA, March 20-21, 2012.

6. Searches all content (people, publications, concepts, etc.) Uses stemming and thesaurus for term expansion Uses ontology for search filters, faceting, expanding and ranking of search results Search matches literals, not profiles Profiles RNS 1.0 Search

7. Example #1 (Person) My research is in social network analysis and bibliometrics. overview

8. Search for “Bibliometrics” (Person) My research is in social network analysis and bibliometrics. overview What is the chance the person is an expert in “bibliometrics”?

9. Search Relevance Score Text Weight – Probability that a literal node L is relevant to the search phrase S Connection Weight – Probability that node N is connected to node L through property P Search Weight – Probability that N is relevant to the search phrase assuming N is connected to L through P and L is relevant to the search phrase Relevance Score – Search Weight * Connection Weight * Text Weight

10. Text Weight (Person) My research is in social network analysis and bibliometrics. overview 0.2 How relevant is “bibliometrics” to this literal?

11. Connection Weight (Person) My research is in social network analysis and bibliometrics. overview 1.0 Is this really this person’s overview?

12. Search Weight (Person) My research is in social network analysis and bibliometrics. overview 0.5 Is the person really an expert in the topics mentioned in her overview?

13. Relevance Score (Person) My research is in social network analysis and bibliometrics. overview 0.5 * 1.0 * 0.2 = 0.1 There is a 10% chance the person is an expert in “bibliometrics” based only on this overview

14. Example #2 (Person) My research is in social network analysis and bibliometrics. overview Bibliometric Analysis researchArea What is the chance the person is an expert in “bibliometrics”?

15. Text Weight (Person) My research is in social network analysis and bibliometrics. overview Bibliometric Analysis researchArea 0.2 0.5 How relevant is “bibliometrics” to these literals?

16. Connection Weight (Person) My research is in social network analysis and bibliometrics. overview Bibliometric Analysis researchArea 1.0 Is this really the person’s overview, and is this really the person’s research area? 0.3

17. Search Weight (Person) My research is in social network analysis and bibliometrics. overview Bibliometric Analysis researchArea 0.5 Is this person an expert in the topics in her overview, and in the areas she actually publishes about? 0.8

18. Relevance Score (Person) My research is in social network analysis and bibliometrics. overview Bibliometric Analysis researchArea 0.5 * 1.0 * 0.2 = 0.10 This person is an expert in “bibliometrics” with probabilities of 10% based only on the overview and 12% only on the researchArea 0.8 * 0.3 * 0.5 = 0.12

19. Relevance Score (Person) My research is in social network analysis and bibliometrics. overview Bibliometric Analysis researchArea 0.5 * 1.0 * 0.2 = 0.10 There is a 20.8% chance the person is an expert based on both the overview and the researchArea 0.8 * 0.3 * 0.5 = 0.12 P(Expert) = 1 - P(Not an Expert) = 1 - (1 - 0.1) * (1 - 0.12) = 0.208

20. Example #3 – Find “Weber” (Person) “Griffin” “Weber” (Person) (Authorship) (Article) “Weber, Smith” “Weber G. 2011. 3(1):147” firstName lastName label linkedIR author similarTo (Concept) subject “Sturge-Weber Syndrome” label

21. Example #3 – Find “Weber” (Person) “Griffin” “Weber” (Person) (Authorship) (Article) “Weber, Smith” “Weber G. 2011. 3(1):147” firstName lastName label linkedIR author similarTo (Concept) subject “Sturge-Weber Syndrome” label

22. Text Weight (Person) (Authorship) (Article) lastName label linkedIR author similarTo (Concept) subject label 0 1.0 0.5 0.25 0.33 “Weber” “Weber, Smith” “Weber G. 2011. 3(1):147” “Sturge-Weber Syndrome” “Griffin” firstName

23. Connection Weight (Person) (Authorship) (Article) lastName label linkedIR author similarTo (Concept) subject label “Weber” “Weber, Smith” “Weber G. 2011. 3(1):147” “Sturge-Weber Syndrome” “Griffin” firstName 1.0 0.5 0.4 0.3 0.5 1.0

24. Search Weight (Person) (Authorship) (Article) lastName label linkedIR author similarTo (Concept) subject label “Weber” “Weber, Smith” “Weber G. 2011. 3(1):147” “Sturge-Weber Syndrome” “Griffin” firstName 0.5 1.0 0.01 0 0.5 1.0

25. 1.0*1.0*0.25 Relevance Score (Person) (Authorship) (Article) lastName label linkedIR author similarTo (Concept) subject label “Weber” “Weber, Smith” “Weber G. 2011. 3(1):147” “Sturge-Weber Syndrome” “Griffin” firstName 0.5*1.0*0 1.0*1.0*1.0 1.0*1.0*0.5 1.0*0.5* 0.4*0.01* 0*0.3* 0.5*0.5* 1.0*1.0*0.33

26. 0.31 Relevance Score (Person) (Authorship) (Article) lastName label linkedIR author similarTo (Concept) subject label “Weber” “Weber, Smith” “Weber G. 2011. 3(1):147” “Sturge-Weber Syndrome” “Griffin” firstName 0.5 0.16 1.0 0.33

27. treatments for lung cancer Compare to thesaurus: Select best parsing – treatments for lung cancer – treatments for lung cancer  Search Phrase Parsing 1Cancer 1Neoplasm 2Cancer of the Lung 2Lung Cancer 2Lung Neoplasm

28. treatments for lung cancer Remove stop words not in recognized phrases – treatments for lung cancer – treatments lung cancer Stemming for words not in recognized phrases – treatment* lung cancer Expand using thesaurus – “treatment*” AND (“cancer of the lung” OR “lung cancer” OR “lung neoplasm”) Search Phrase Parsing

29. Pagination – Offset, Limit Filter by class – Example: only return people Filter by property – Example: “cancer” and lastName = “Smith” – Example: “cancer” and NOT facultyRank = “Full Prof.” Sort by property – Example: sort by lastName, firstName, middleName – Default: relevance score, label Search Options

30. treatments for lung cancer http://xmlns.com/foaf/0.1/Person Smith 0 25 Search Request XML