Taxonomy and Corpus Assessment: Using Visualization

Taxonomy and Corpus Assessment: Using Visualization Kevin W. Boyack DHUG Meeting, February 9, 2012

SciTech Strategies, Inc.
Richard Klavans, Henry Small, Kevin Boyack
Decades of experience in
Citation analysis
Science mapping
Metrics
People associate us with “visualization”
Current interests
Structure and dynamics of STI (science, technology, innovation)
Impacts of funding on structure and dynamics
Emergence, evolution
How does access to full text change or view of or ability to model the above?

Our maps SciVal Spotlight ® UCSD Map of Science

Recurring questions
Does our coverage match our mission, vision, or charter?
Is our thesaurus / taxonomy up to date / sufficient?
Where is the industry headed? Does it vary by sector? Can we forecast our own direction?
What are the trends – are topics emerging / dying?
Can we use our own data to answer these questions?
Can we become smarter about our data and potential markets using our collection in new ways?
Can we enhance our data?

Visualization
Can visualization play a part in the answers?
Yes, but just what is visualization?
It’s not just the picture!
It’s the entire process that leads to the picture
Define the question (content or context)
Data selection
Data cleaning and processing
Analysis
Then, you can create the visual

Some questions with visual answers
From a thesaurus / taxonomy perspective
What terms are too broadly defined?
How do actual topical relationships differ from the thesaurus structure?
What terms could / should be added?
From a vendor / publisher perspective
Which topical areas form our core? periphery?
Where is the coverage dense? thin?
Which topical areas are most active? least active?
Which topical areas seem to be emerging? declining?
Which topical areas are interrelated? isolated?
What are the overlaps between journals / segments?
Where are the potential expansion points?

Content vs. context
Content
Requires only your own data
Focused
Computationally tractable
Good if you don’t need context
Context
Requires more data than just your own
Broad
More computationally intensive
but ….
You can see results in a broader (competitive) context

Content examples

Thesaurus structure / balance

Term relationship structure

Society overlay on term structure

Partitioning and overlaps

Context examples
Requires more data than just your own
Adding other focused datasets
Placing your data in the context of a global dataset (e.g. Scopus)
We have done both
A micro-structural model of a global database lends itself to analysis at the micro-level
Micro-level indicators and trends

Term expansion (IEEE, USPTO, MeSH)

Global micro-model (Scopus) 2010 fileyear 1.8 M documents 115k clusters Cluster histories Cluster metrics Cluster memberships

PubMed overlay

Cluster histories (threads)

Map of threads containing IEEE literature

IEEE Society overlays

Science mapping
30-40 year tradition of science mapping
Well-established methodologies
Current computing power and data availability enable large scale mapping and analysis
Science maps can/have been created using
Articles, Journals, Authors, Terms
A map can be thought of as a visual representation of a classification system provided …
The classification system DOES contain a relationship structure
Maps used for communication, strategy, planning, evaluation …

Visualization choices Choice Trade-offs Comments Data source - Free vs. Costly - Single vs. Multiple Sources - Content vs. Context - Citation data isn’t free - De-duplication isn’t trivial - Coverage comes at a cost Unit of analysis - Breadth vs. Detail
Base on research question
Don’t base on data available
Sample size - Specialty vs. All of science - Content vs. Context - Single year vs. Multiple years
Mapping ALL is costly
Specialty maps lack context
- Stability or instability?
Similarity approach
Simple vs. Complex
Citation vs. Text vs. Hybrid
- Threshold vs. Accuracy
Comp cost: Index < Vector
Hybrid costly, but likely best
- How much is really needed?
Partitioning / Layout
Simple vs. Complex
- Accuracy vs. Useability
Simple often size limited
Is intuition satisfied?
- Are distributions reasonable?
- Useful levels of aggregation?

Visualization choices
Are often made based on what is available
Data, algorithms, expertise
When they should be made based on
The research question or application
Balancing of the applicable trade-offs

Summary
Term and document spaces can be mapped effectively
The mapped space can be used to show distributions and trends that give answers to questions regarding
Context
Distributions and overlaps
Trends
Etc.
Historically, this has all been done from metadata
You all have full text – a gold mine of information
How will you use it?

Thank you!

Taxonomy and Corpus Assessment: Using Visualization

by accessinnovations on Feb 14, 2012

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Taxonomy and Corpus Assessment: Using Visualization — Presentation Transcript