sci know mine 2013: what can we learn from topic modeling on 350m academic documents?
DESCRIPTION
Mendeley TalkTRANSCRIPT
What can we learn from topic modeling on 350M
documents?
William GunnHead of Academic OutreachMendeley@mrgunn – https://orcid.org/0000-0002-3555-2054
Based in London, Mendeley is researchers, graduates and software developers from...
The opposite problem
We have the papers (400M) and are looking for the best way to turn them into structured knowledge.
We have useful triage indicators - #altmetrics, reproducibility
You have great use cases
...and aggregatesdata in the cloud
Mendeley extracts research data…
Collecting rich signals from domain experts.
Rich user profile data
TEAM Projectacademic 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/
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!
Distribution of Topics
BioPhy
s
Engine
er
Comp
Sci
Psych
& E
du
Busine
ss Law
Other
0%5%
10%15%20%25%30%35%
Subcategories of Comp. Sci.
AI HCI Info Sci Software Eng
Networks0%
5%
10%
15%
20%
Generated topics – Comp. Sci.
Generated Topics - Biology
Categorization is imperfect
Categorization As A ProcessThing
Process
Reaction
Catalysis
Enzymatic
Categorization As A ProcessThing
Process
Reaction
Catalysis
Enzymatic
Categories change over time
Can we assist triage?
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/
Metrics as a discovery tool
We didn’t see that a target is more likely to be validated if it was reported in ten publications or in two publications
“NATURE REVIEWS DRUG DISCOVERY 10, 712 (SEPTEMBER 2011)
Either the results were reproducible and showed transferability in other models, or even a 1:1 reproduction of published experimental procedures revealed inconsistencies between published and in-house data
“NATURE REVIEWS DRUG DISCOVERY 10, 712 (SEPTEMBER 2011)
Amgen: 47 of 53 “landmark” oncology publications could not be reproduced.
Bayer: 43 of 67 oncology & cardiovascular projects were based on contradictory results
Dr. John Ioannidis: 432 publications purporting sex differences in hypertension, multiple sclerosis, or lung cancer. Only one data set was reproducible.
There is no Gold Standard
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
Make it porous & part of the web.
Our success as a crowdsourcing platform is largely due to our openness & end-user usefulness.
Communities must be open if they are to thrive.
