William Gunn is the Head of Academic Outreach at Mendeley. He discusses how topic modeling on 350M documents can provide insights. Topic modeling allows computers to analyze the semantics versus just the syntax of documents by grouping words that frequently occur together into topics. This reveals the distribution of topics across different subject areas like biology, physics, computer science, and psychology. It can also generate more specific topics within fields and show how categorization is an imperfect process that changes over time.

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

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

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

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.

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

6. Rich user profile data

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/

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!

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

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

12. Generated topics – Comp. Sci.

13. Generated Topics - Biology

14. Categorization As A Process
Thing
Process
Reaction
Catalysis
Enzymatic

15. Categorization As A Process
Thing
Process
Reaction
Catalysis
Enzymatic

16. Categorization is imperfect

17. Cateories change over time

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/

22. Metrics as a discovery tool

23. Google Analytics for Research

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

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.

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