What can we learn from entity extraction and topic modeling across 350M documents.

My presentation at VIVO 2013 on topic modeling and entity extraction.

Transcript

1. What can we learn from topic
   modeling on 350M documents?
   William Gunn
   Head of Academic Outreach
   Mendeley
   @mrgunn – https://orcid.org/0000-0002-3555-2054
   

   View Slide

2. Who am I?
   
   PhD Biomedical Science
   
   I've been active in online science
   communities since 1995
   
   Established the community program at
   Mendeley – 1700 advisors from 650
   schools in 60 countries.
   
   Lead the outreach to librarian, academic
   research, and tech communities
   

   View Slide

3. Based in London, Mendeley is
   researchers, graduates and software
   developers from...
   

   View Slide

4. Two new approaches
    Embed a tool within the researcher
   workflow to capture data
    Capture new kinds of data – usage
   of research objects, not just
   citations of papers.
   

   View Slide

5. ...and aggregates
   data in the cloud
   Mendeley
   extracts
   research data…
   Collecting rich signals
   from domain experts.
   

   View Slide

6. Rich user profile data
   

   View Slide

7. TEAM Project
   academic knowledge management solutions
   • Algorithms to determine the content similarity of academic papers
   • Performing text disambiguation and entity recognition to
   differentiate between and relate similar in-text entities and authors
   of research papers.
   • Developing semantic technologies and semantic web languages with
   the focus of metadata integration/validation
   • Investigate profiling and user analysis technologies, e.g. based on
   search logs and document interaction.
   • We will also improve folksonomies and through that, ontologies of
   text.
   • Finally, tagging behaviour will be analysed to improve tag
   recommendations and strategies.
   • http://team-project.tugraz.at/blog/
   

   View Slide

8. Semantics vs. Syntax
   • Language expresses semantics via syntax
   • Syntax is all a computer sees in a research
   article.
   • How do we get to semantics?
   •Topic Modeling!
   

   View Slide

9. Distribution of Topics
   0%
   5%
   10%
   15%
   20%
   25%
   30%
   35%
   Bio Phys Engineer Comp
   Sci
   Psych &
   Edu
   Business Law Other
   

   View Slide

10. Subcategories of Comp. Sci.
    0%
    5%
    10%
    15%
    20%
    AI HCI Info Sci Software
    Eng
    Networks
    

    View Slide

11. View Slide

12. Generated topics – Comp. Sci.
    

    View Slide

13. Generated Topics - Biology
    

    View Slide

14. Categorization As A Process
    Thing
    Process
    Reaction
    Catalysis
    Enzymatic
    

    View Slide

15. Categorization As A Process
    Thing
    Process
    Reaction
    Catalysis
    Enzymatic
    

    View Slide

16. Categorization is imperfect
    

    View Slide

17. Cateories change over time
    

    View Slide

18. Code Project
    Use case = mining research papers for facts
    to add to LOD repositories and light-weight
    ontologies.
    • Crowd-sourcing enabled semantic enrichment & integration
    techniques for integrating facts contained in unstructured
    information into the LOD cloud
    • Federated, provenance-enabled querying methods for fact
    discovery in LOD repositories
    • Web-based visual analysis interfaces to support human based
    analysis, integration and organisation of facts
    • Socio-economic factors – roles, revenue-models and value
    chains – realisable in the envisioned ecosystem.
    • http://code-research.eu/
    

    View Slide

19. View Slide

20. View Slide

21. View Slide

22. Metrics as a discovery tool
    

    View Slide

23. Google Analytics for Research
    

    View Slide

24. Building a reproducibility dataset
    • Mendeley and Science Exchange have
    started the Reproducibility Initiative
    • working with Figshare & PLOS to host data
    & replication reports
    • building open datasets backing high-
    impact work
    • extending the “executable paper” concept
    to biomedical research
    

    View Slide

25. Make it porous & part of the
    web.
    
    All these examples show that the main
    motivation for people to get data (pictures,
    bookmarks, etc) off their computers and
    on the web is because it helps them find
    more of the same.
    
    Communities must be open if they are to
    thrive.
    

    View Slide

26. www.mendeley.com
    william.gunn@mendeley.com
    @mrgunn
    

    View Slide