SlideShare une entreprise Scribd logo
1  sur  43
Télécharger pour lire hors ligne
TEXT MINING, TERM MINING,
AND VISUALIZATION
IMPROVING THE IMPACT OF SCHOLARLY
PUBLISHING
MONDAY 16 APRIL 2012
NICE, FRANCE
Marjorie M.K. Hlava, President
Jay Ven Eman, CEO
Access Innovations, Inc.
mhlava@accessinn.com
J_ven_eman@accessinn.com
1
What we will cover today
• Term and Text Mining
• The basics of visualization
• Case studies
• Using subject terms as metrics
• Applications
• Visualizing the results
Definitions
• Term Mining - a systematic comparison processing
algorithmic method to find patterns in text
• Text Mining – using controlled vocabulary tags in text to
find patterns and directions
• Term & text mining
Many similarities
Can be complimentary; not mutually exclusive
Term mining
• Precise
Meaningful semantic relationships; contextual
Replicable; repeatable; consistent
Vetted; controlled
Based on a controlled vocabulary
Trends; gaps; relationship analysis; visualizations
Less data processing load
Text mining
Algorithmic; formulaic
Neural nets, statistical, latent semantic, co -
occurrence
Serendipitous relationships
Sentiment; hot topics; trends
False drops; noise;
Misleading semantic relationships
Heavy processing load
Why take a visual look?
• Humans can process information 17 times faster in visual
presentations
• Now data can be analyzed, manipulated and presented as visual
displays.
• To see the trends effectively we need to make the data into rich
graph-able formats
6
Visualization of data
• Needs
− Measurement
− Metrics
− Numbers
• Shows
− Adjacency
− Relationships
− Trends
− Co – occurrence
− Conceptual distance
• Is richer with
− Linking
− Semantic enrichment
− Classification
• Supports
− Forecasting
− Trend analysis
− Segmentation
− Distribution
7
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Man’s attention to
visual display to convey
knowledge is ancient
8
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
The art in maps
is a
longstanding
tradition
9
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Super imposing data is now common
A mash up example
10
Traffic Injury Map
UK Data Archive
US National Highway
Safety Administration
Google Maps Base
Accident categories include
children
automobile
bicycle
etc.
Data
time
place
type
Source:
JISC TechWatch: Data Mash-ups September 2010
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Mash up of bird flight migrations and
weather patterns
http://www.youtube.com/watch?v=uPff1t4pXiI&feature=youtu.be
11
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
http://www.youtube.com/watch?v=nokQBjk1s_8&feature=player_embedded
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
How does it work?
Develop controlled vocabulary
» Prefer one with hierarchy
Apply to full text
» Or to the “heads”
Decide on data points to convey information
Divide the XML into graphable sections
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Start with data – like this XML file
14
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Index or tag using subject terms from
thesaurus or taxonomy
date, category, taxonomy term, frequency
15
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Many views of one set of data
16
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Load to a visualization program
Like Prefuse
17
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Or Pajek
18
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 19
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
National Information Center
for Educational Media
Albuquerque’s own
» Sandia developed VxInsight
» Access Innovations = NICEM
Same data – several views
Primary and Secondary Education in US
Shows the US Valley of Science
Little Science taught in elementary years
20
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Using visualization to show
From a society / publisher perspective
» Identify Core, Boundary and Cross Border
» Provides Indicators
Activity
Growth
Relatedness
Centrality
» Locates Journal domains
From a thesaurus perspective
» Identifies terms that are too broadly defined
» Potential Improvements in thesaurus structure using topic
structures
23
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Case Study:
Mapping IEEE thesaurus space
We are interested in an expanded map that
includes adjacencies to the IEEE data
» Expanded term set shows adjacent white space;
opportunities for expansion
Overlaps and edges of the science
» We need comparison data
Learn the directions in the field
» Low occurrence rate in IEEE documents?
» Linkage to terms in IEEE documents?
Where do we find these terms? How can we
add them?
24
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
The process
Built a rule base to auto index IEEE content
» “90 % accuracy out of the box on journal data”*
» “80% out of the box on proceedings data”*
The overlapping data sets
» Auto indexed 1.2 million Xplore records
» Auto indexed 10 years of US Patent data
» Auto indexed 10 years of Medline
Term sets used
» IEEE thesaurus terms rule base
» Medical Subject Headings (MeSH) (and simple rule base)
» Defense Technical Information Center (DTIC) Thesaurus (
and simple rule base)
» Similar level of detail to current IEEE thesaurus terms
25
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Defining expanded term space
26
IEEE
2kterms
1.2M documents
1. The data - Select related corpus
14kDTIC
475k patents
24kMeSH
PubMed
525k docs
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Defining expanded term space
27
IEEE
2kterms
1.2M documents
2. Identify related terms
Use the IEEE Thesaurus to index the three collections
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Defining expanded term space
28
IEEE
2kterms
1.2M documents
2. Identify related terms
Use MESH and DTIC to also index the three collections
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 29
IEEE
2kterms
1.2M documents
3. Resulting term set
The co-indexed items from the three collections
Defining expanded term space
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 30
4. Term:Term Matrix
Where do the articles and their indexing intersect?
Defining expanded term space
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 31
Visualization Strategies
Matrix
Visualization
Software
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 32
All data up-posted to the top level
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 33
Many map options
IEEE ExperiencePrevious Experience
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 34
Sensors
Council
Nucl Plasma
Sci Soc
Nanotech
Council
Ultrason,
Ferro …
Prod Saf
Engng Soc
Oceanic
Engng Soc
Geosci Rem
Sens Soc
Council
Supercond
Compon,
Packag …
Instr
Measur Soc
Magnetics
Soc
Dielectr El
Insul Soc
Electromag
Compat Soc
Antennas
Propag Soc
Power
Electron Soc
Electron
Dev Soc
Circuits &
Systems
Power &
Energy Soc
Industry
Appl Soc
Solid St
Circuits Soc
Industr
Electr Soc
Microwave
Theory Soc
Aerosp
Electr
Sys Soc
Sys Man
Cyber
Society
Computer
Intelligence
Society
Systems
Council
Reliability
Society Education
Society
Prof
Commun
Society
Computer
Society
Robot
Autom Soc
Social
Impl Techn
Council Electr
Design Auto
Signal
Proc Soc
Intell Transp
Sys Soc
Commun
Soc
Info Theory
Soc
Vehicular
Techn Soc
Consumer
Electr Soc
Broadcast
Techn Soc
Photonics
Soc
Eng Med
Biol Sci
IEEE Portfolio
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 35
Radial Visualization
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 36
Publication Strategy
JASIST reference
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 37
Conference Strategy
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 38
Turbines
Measurement
Circuits
Amplifiers
Displays
Games
Toys
Flow
Cooling
Heating
Components
Gearing
Brakes
Dynamics
Vehicles,
Parts
Disk
Optics
Photochem
Molding
Conductors
Coatings
Lasers
Lamps
Motors
Plants,
Micro-orgs
Control
Boats
Oilfield
Services
Med Instruments
Welding
Conveyers
Rubber
Acyclic Comp
Footwear
Lubricants
Radiology
Catalysis
Macromolecules
Sprayers
Electrochem
Fitness
Hygiene
Cleaning
Printing
Paper
IC Engines
Magn/Elect
Magnets
Textiles
Layers
Medical
Devices
Clocks
Pipes
Valves
Blasting
Cables
Appliances
Outerwear
Exhaust
Pumps
Packaging
Aircraft
Semiconductors
Use a Thesaurus to Label Maps
Agriculture
Food
Consumer
Products
Construction
Automotive +
Defense
Industrial
Products
Leisure
Energy
Telecom Computer
HW/SW
Electronics
Chemicals
Pharma
Metals
Health Care
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Questions Answered
Is there a way, using our own information, to forecast our
direction?
Where is the industry headed? What about by technology
sector?
Does our coverage match our mission and vision?
Can we become smarter about our data and potential
markets using our collection in new ways?
Are the societies publishing and talking about what their
charter indicates they cover?
What are the trends – are topics emerging/cooling?
Can we use technology and our own data to explore these
questions while enhancing our data?
39
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
The research team
Access Innovations / Data Harmony
» Founded in 1978
» Data enrichment and normalization
» Suite of Semantic Enrichment tools
SciTechStrategies
» Understanding data through visualization
IEEE Indexing & Abstracting Group
40
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
We looked at
visualization of data
Finding the Metrics
» Measurement
» Numbers
» Terms as indicators
Ways to show
» Adjacency
» Relationships
» Trends
» Co – occurrence
» Conceptual distance
How to enrich with
» Linking
» Semantic enrichment
» Classification
Maps supporting
» Forecasting
» Trend analysis
» Segmentation
» Distribution
41
Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
Effective maps require
Contextual data
Detailed data
Classification methods
At least two directions in the matrix
A little art for fun
42
43
It just takes a little imagination
Thank you
Marjorie M.K. Hlava
President
mhlava@accessinn.com
Jay Ven Eman, CEO
J_ven_eman@accessinn.com
, Access Innovations
505-998-0800

Contenu connexe

Tendances

AI-SDV 2021: Dolcera
AI-SDV 2021: DolceraAI-SDV 2021: Dolcera
AI-SDV 2021: Dolcera
Dr. Haxel Consult
 
II-SDV 2012 From (Text) Mining to Models: Applying Large-Scale Text Mining on...
II-SDV 2012 From (Text) Mining to Models: Applying Large-Scale Text Mining on...II-SDV 2012 From (Text) Mining to Models: Applying Large-Scale Text Mining on...
II-SDV 2012 From (Text) Mining to Models: Applying Large-Scale Text Mining on...
Dr. Haxel Consult
 
Toward F.A.I.R. Pharma. PhUSE Linked Data Initiatives Past and Present
Toward F.A.I.R. Pharma. PhUSE Linked Data Initiatives Past and PresentToward F.A.I.R. Pharma. PhUSE Linked Data Initiatives Past and Present
Toward F.A.I.R. Pharma. PhUSE Linked Data Initiatives Past and Present
Tim Williams
 

Tendances (20)

II-SDV 2016 Srinivasan Parthiban - KOL Analytics from Biomedical Literature
II-SDV 2016 Srinivasan Parthiban - KOL Analytics from Biomedical LiteratureII-SDV 2016 Srinivasan Parthiban - KOL Analytics from Biomedical Literature
II-SDV 2016 Srinivasan Parthiban - KOL Analytics from Biomedical Literature
 
II-SDV 2017: Spotting the Stars in your Galaxy of Patent Data
II-SDV 2017: Spotting the Stars in your Galaxy of Patent DataII-SDV 2017: Spotting the Stars in your Galaxy of Patent Data
II-SDV 2017: Spotting the Stars in your Galaxy of Patent Data
 
Fair webinar, Ted slater: progress towards commercial fair data products and ...
Fair webinar, Ted slater: progress towards commercial fair data products and ...Fair webinar, Ted slater: progress towards commercial fair data products and ...
Fair webinar, Ted slater: progress towards commercial fair data products and ...
 
PA webinar on benefits & costs of FAIR implementation in life sciences
PA webinar on benefits & costs of FAIR implementation in life sciences PA webinar on benefits & costs of FAIR implementation in life sciences
PA webinar on benefits & costs of FAIR implementation in life sciences
 
Application of recently developed FAIR metrics to the ELIXIR Core Data Resources
Application of recently developed FAIR metrics to the ELIXIR Core Data ResourcesApplication of recently developed FAIR metrics to the ELIXIR Core Data Resources
Application of recently developed FAIR metrics to the ELIXIR Core Data Resources
 
NISO/NFAIS Joint Virtual Conference: Connecting the Library to the Wider Wor...
NISO/NFAIS Joint Virtual Conference:  Connecting the Library to the Wider Wor...NISO/NFAIS Joint Virtual Conference:  Connecting the Library to the Wider Wor...
NISO/NFAIS Joint Virtual Conference: Connecting the Library to the Wider Wor...
 
Knowledge graphs ilaria maresi the hyve 23apr2020
Knowledge graphs   ilaria maresi the hyve 23apr2020Knowledge graphs   ilaria maresi the hyve 23apr2020
Knowledge graphs ilaria maresi the hyve 23apr2020
 
Trustworthy AI and Open Science
Trustworthy AI and Open ScienceTrustworthy AI and Open Science
Trustworthy AI and Open Science
 
AI-SDV 2021: Dolcera
AI-SDV 2021: DolceraAI-SDV 2021: Dolcera
AI-SDV 2021: Dolcera
 
Fairification experience clarifying the semantics of data matrices
Fairification experience clarifying the semantics of data matricesFairification experience clarifying the semantics of data matrices
Fairification experience clarifying the semantics of data matrices
 
NISO/NFAIS Joint Virtual Conference: Connecting the Library to the Wider Wor...
NISO/NFAIS Joint Virtual Conference:  Connecting the Library to the Wider Wor...NISO/NFAIS Joint Virtual Conference:  Connecting the Library to the Wider Wor...
NISO/NFAIS Joint Virtual Conference: Connecting the Library to the Wider Wor...
 
Knowledge graph construction for research & medicine
Knowledge graph construction for research & medicineKnowledge graph construction for research & medicine
Knowledge graph construction for research & medicine
 
II-SDV 2017: Towards Semantic Search at the European Patent Office
II-SDV 2017: Towards Semantic Search at the European Patent OfficeII-SDV 2017: Towards Semantic Search at the European Patent Office
II-SDV 2017: Towards Semantic Search at the European Patent Office
 
II-SDV 2012 From (Text) Mining to Models: Applying Large-Scale Text Mining on...
II-SDV 2012 From (Text) Mining to Models: Applying Large-Scale Text Mining on...II-SDV 2012 From (Text) Mining to Models: Applying Large-Scale Text Mining on...
II-SDV 2012 From (Text) Mining to Models: Applying Large-Scale Text Mining on...
 
II-PIC 2017: Product Presentation LexisNexis
II-PIC 2017: Product Presentation LexisNexisII-PIC 2017: Product Presentation LexisNexis
II-PIC 2017: Product Presentation LexisNexis
 
Semantics and linked data at astra zeneca
Semantics and linked data at astra zenecaSemantics and linked data at astra zeneca
Semantics and linked data at astra zeneca
 
Toward F.A.I.R. Pharma. PhUSE Linked Data Initiatives Past and Present
Toward F.A.I.R. Pharma. PhUSE Linked Data Initiatives Past and PresentToward F.A.I.R. Pharma. PhUSE Linked Data Initiatives Past and Present
Toward F.A.I.R. Pharma. PhUSE Linked Data Initiatives Past and Present
 
Workshop - finding and accessing data - Cambridge August 22 2016
Workshop - finding and accessing data - Cambridge August 22 2016Workshop - finding and accessing data - Cambridge August 22 2016
Workshop - finding and accessing data - Cambridge August 22 2016
 
Knowledge Graphs : Shaping Our Data Future
Knowledge Graphs : Shaping Our Data FutureKnowledge Graphs : Shaping Our Data Future
Knowledge Graphs : Shaping Our Data Future
 
Linked Data for Biopharma
Linked Data for BiopharmaLinked Data for Biopharma
Linked Data for Biopharma
 

En vedette

ICIC 2014 From SureChem to SureChEMBL
ICIC 2014 From SureChem to SureChEMBLICIC 2014 From SureChem to SureChEMBL
ICIC 2014 From SureChem to SureChEMBL
Dr. Haxel Consult
 
II-SDV 2013 Big Data Triage with Text Analytics
II-SDV 2013 Big Data Triage with Text AnalyticsII-SDV 2013 Big Data Triage with Text Analytics
II-SDV 2013 Big Data Triage with Text Analytics
Dr. Haxel Consult
 
II-SDV 2012 Getting beyond Keywords: Using Conceptual Representations of Text...
II-SDV 2012 Getting beyond Keywords: Using Conceptual Representations of Text...II-SDV 2012 Getting beyond Keywords: Using Conceptual Representations of Text...
II-SDV 2012 Getting beyond Keywords: Using Conceptual Representations of Text...
Dr. Haxel Consult
 
II-SDV 2012 Bringing Together Tools and Techniques for Patent Analysis: The H...
II-SDV 2012 Bringing Together Tools and Techniques for Patent Analysis: The H...II-SDV 2012 Bringing Together Tools and Techniques for Patent Analysis: The H...
II-SDV 2012 Bringing Together Tools and Techniques for Patent Analysis: The H...
Dr. Haxel Consult
 
II-SDV 2012 Open Source Platform & Cloud Platform for Information Analysis
II-SDV 2012 Open Source Platform & Cloud Platform for Information AnalysisII-SDV 2012 Open Source Platform & Cloud Platform for Information Analysis
II-SDV 2012 Open Source Platform & Cloud Platform for Information Analysis
Dr. Haxel Consult
 
II-SDV 2012 Dealing with Large Data Volumes in Statistical Analysis and Text ...
II-SDV 2012 Dealing with Large Data Volumes in Statistical Analysis and Text ...II-SDV 2012 Dealing with Large Data Volumes in Statistical Analysis and Text ...
II-SDV 2012 Dealing with Large Data Volumes in Statistical Analysis and Text ...
Dr. Haxel Consult
 
II-SDV 2012 Mining Opinion from Twitter
II-SDV 2012 Mining Opinion from TwitterII-SDV 2012 Mining Opinion from Twitter
II-SDV 2012 Mining Opinion from Twitter
Dr. Haxel Consult
 
II-SDV 2012 From Discovery to Delivery : The Emergence of Mainstream Applicat...
II-SDV 2012 From Discovery to Delivery : The Emergence of Mainstream Applicat...II-SDV 2012 From Discovery to Delivery : The Emergence of Mainstream Applicat...
II-SDV 2012 From Discovery to Delivery : The Emergence of Mainstream Applicat...
Dr. Haxel Consult
 
I-SDV 2012 What Information Tools can you Imagine for the Future? - Conferenc...
I-SDV 2012 What Information Tools can you Imagine for the Future? - Conferenc...I-SDV 2012 What Information Tools can you Imagine for the Future? - Conferenc...
I-SDV 2012 What Information Tools can you Imagine for the Future? - Conferenc...
Dr. Haxel Consult
 
II-SDV 2013 Text Mining Diverse Data
II-SDV 2013 Text Mining Diverse DataII-SDV 2013 Text Mining Diverse Data
II-SDV 2013 Text Mining Diverse Data
Dr. Haxel Consult
 
II-SDV 2012 Towards Unified Access Systems for Data Exploration
II-SDV 2012 Towards Unified Access Systems for Data ExplorationII-SDV 2012 Towards Unified Access Systems for Data Exploration
II-SDV 2012 Towards Unified Access Systems for Data Exploration
Dr. Haxel Consult
 
II-SDV 2012 Patent Overlay Mapping
II-SDV 2012 Patent Overlay MappingII-SDV 2012 Patent Overlay Mapping
II-SDV 2012 Patent Overlay Mapping
Dr. Haxel Consult
 
II-SDV 2014 Product Presentations Innography
II-SDV 2014 Product Presentations InnographyII-SDV 2014 Product Presentations Innography
II-SDV 2014 Product Presentations Innography
Dr. Haxel Consult
 
II-SDV 2013 Challenges in Visualizing Pharmaceutical Business Information
II-SDV 2013 Challenges in Visualizing Pharmaceutical Business InformationII-SDV 2013 Challenges in Visualizing Pharmaceutical Business Information
II-SDV 2013 Challenges in Visualizing Pharmaceutical Business Information
Dr. Haxel Consult
 
II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev...
II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev...II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev...
II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev...
Dr. Haxel Consult
 
II-SDV 2013 Customising Statistics for Sharper Analysis
II-SDV 2013 Customising Statistics for Sharper AnalysisII-SDV 2013 Customising Statistics for Sharper Analysis
II-SDV 2013 Customising Statistics for Sharper Analysis
Dr. Haxel Consult
 

En vedette (20)

ICIC 2014 From SureChem to SureChEMBL
ICIC 2014 From SureChem to SureChEMBLICIC 2014 From SureChem to SureChEMBL
ICIC 2014 From SureChem to SureChEMBL
 
II-SDV 2013 Big Data Triage with Text Analytics
II-SDV 2013 Big Data Triage with Text AnalyticsII-SDV 2013 Big Data Triage with Text Analytics
II-SDV 2013 Big Data Triage with Text Analytics
 
II-SDV 2012 Getting beyond Keywords: Using Conceptual Representations of Text...
II-SDV 2012 Getting beyond Keywords: Using Conceptual Representations of Text...II-SDV 2012 Getting beyond Keywords: Using Conceptual Representations of Text...
II-SDV 2012 Getting beyond Keywords: Using Conceptual Representations of Text...
 
II-SDV 2012 Bringing Together Tools and Techniques for Patent Analysis: The H...
II-SDV 2012 Bringing Together Tools and Techniques for Patent Analysis: The H...II-SDV 2012 Bringing Together Tools and Techniques for Patent Analysis: The H...
II-SDV 2012 Bringing Together Tools and Techniques for Patent Analysis: The H...
 
II-SDV 2012 Open Source Platform & Cloud Platform for Information Analysis
II-SDV 2012 Open Source Platform & Cloud Platform for Information AnalysisII-SDV 2012 Open Source Platform & Cloud Platform for Information Analysis
II-SDV 2012 Open Source Platform & Cloud Platform for Information Analysis
 
II-SDV 2012 Dealing with Large Data Volumes in Statistical Analysis and Text ...
II-SDV 2012 Dealing with Large Data Volumes in Statistical Analysis and Text ...II-SDV 2012 Dealing with Large Data Volumes in Statistical Analysis and Text ...
II-SDV 2012 Dealing with Large Data Volumes in Statistical Analysis and Text ...
 
II-SDV 2012 Mining Opinion from Twitter
II-SDV 2012 Mining Opinion from TwitterII-SDV 2012 Mining Opinion from Twitter
II-SDV 2012 Mining Opinion from Twitter
 
II-SDV 2012 From Discovery to Delivery : The Emergence of Mainstream Applicat...
II-SDV 2012 From Discovery to Delivery : The Emergence of Mainstream Applicat...II-SDV 2012 From Discovery to Delivery : The Emergence of Mainstream Applicat...
II-SDV 2012 From Discovery to Delivery : The Emergence of Mainstream Applicat...
 
The Final ICIC Programme 2014 in Heidelberg
The Final ICIC Programme 2014 in HeidelbergThe Final ICIC Programme 2014 in Heidelberg
The Final ICIC Programme 2014 in Heidelberg
 
I-SDV 2012 What Information Tools can you Imagine for the Future? - Conferenc...
I-SDV 2012 What Information Tools can you Imagine for the Future? - Conferenc...I-SDV 2012 What Information Tools can you Imagine for the Future? - Conferenc...
I-SDV 2012 What Information Tools can you Imagine for the Future? - Conferenc...
 
II-SDV 2013 Text Mining Diverse Data
II-SDV 2013 Text Mining Diverse DataII-SDV 2013 Text Mining Diverse Data
II-SDV 2013 Text Mining Diverse Data
 
II-SDV 2012 Towards Unified Access Systems for Data Exploration
II-SDV 2012 Towards Unified Access Systems for Data ExplorationII-SDV 2012 Towards Unified Access Systems for Data Exploration
II-SDV 2012 Towards Unified Access Systems for Data Exploration
 
ICIC 2014 The Changing Role of Corporate Information Services at Johnson Matt...
ICIC 2014 The Changing Role of Corporate Information Services at Johnson Matt...ICIC 2014 The Changing Role of Corporate Information Services at Johnson Matt...
ICIC 2014 The Changing Role of Corporate Information Services at Johnson Matt...
 
II-SDV 2012 Patent Overlay Mapping
II-SDV 2012 Patent Overlay MappingII-SDV 2012 Patent Overlay Mapping
II-SDV 2012 Patent Overlay Mapping
 
ICIC 2014 New Product Introduction Minesoft
ICIC 2014 New Product Introduction MinesoftICIC 2014 New Product Introduction Minesoft
ICIC 2014 New Product Introduction Minesoft
 
II-SDV 2014 Product Presentations Innography
II-SDV 2014 Product Presentations InnographyII-SDV 2014 Product Presentations Innography
II-SDV 2014 Product Presentations Innography
 
ICIC 2014 The Future of Pharmaceutical and Life Science Publishing Industries
ICIC 2014 The Future of Pharmaceutical and Life Science Publishing Industries ICIC 2014 The Future of Pharmaceutical and Life Science Publishing Industries
ICIC 2014 The Future of Pharmaceutical and Life Science Publishing Industries
 
II-SDV 2013 Challenges in Visualizing Pharmaceutical Business Information
II-SDV 2013 Challenges in Visualizing Pharmaceutical Business InformationII-SDV 2013 Challenges in Visualizing Pharmaceutical Business Information
II-SDV 2013 Challenges in Visualizing Pharmaceutical Business Information
 
II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev...
II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev...II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev...
II-SDV 2014 Predictive Analytics and the Big Data Challenge (Andrei Grigoriev...
 
II-SDV 2013 Customising Statistics for Sharper Analysis
II-SDV 2013 Customising Statistics for Sharper AnalysisII-SDV 2013 Customising Statistics for Sharper Analysis
II-SDV 2013 Customising Statistics for Sharper Analysis
 

Similaire à II-SDV 2012 Text Mining, Term Mining and Visualization - Improving the Impact of Scholarly Publishing

Semantic Technologies for Big Sciences including Astrophysics
Semantic Technologies for Big Sciences including AstrophysicsSemantic Technologies for Big Sciences including Astrophysics
Semantic Technologies for Big Sciences including Astrophysics
Artificial Intelligence Institute at UofSC
 
Found in Space: Creating and Visualizing IEEE Abstract Space for Publication ...
Found in Space: Creating and Visualizing IEEE Abstract Space for Publication ...Found in Space: Creating and Visualizing IEEE Abstract Space for Publication ...
Found in Space: Creating and Visualizing IEEE Abstract Space for Publication ...
TSoholt
 
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
Being FAIR:  FAIR data and model management SSBSS 2017 Summer SchoolBeing FAIR:  FAIR data and model management SSBSS 2017 Summer School
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
Carole Goble
 
Semantics-enhanced Cyberinfrastructure for ICMSE : Interoperability, Analyti...
Semantics-enhanced Cyberinfrastructure for ICMSE :  Interoperability, Analyti...Semantics-enhanced Cyberinfrastructure for ICMSE :  Interoperability, Analyti...
Semantics-enhanced Cyberinfrastructure for ICMSE : Interoperability, Analyti...
Artificial Intelligence Institute at UofSC
 

Similaire à II-SDV 2012 Text Mining, Term Mining and Visualization - Improving the Impact of Scholarly Publishing (20)

Text Mining, Term Mining, and Visualization - Improving the Impact of Scholar...
Text Mining, Term Mining, and Visualization - Improving the Impact of Scholar...Text Mining, Term Mining, and Visualization - Improving the Impact of Scholar...
Text Mining, Term Mining, and Visualization - Improving the Impact of Scholar...
 
Visualization for Data Analysis: A New Way to Look at Content
Visualization for Data Analysis: A New Way to Look at Content Visualization for Data Analysis: A New Way to Look at Content
Visualization for Data Analysis: A New Way to Look at Content
 
Semantic Technologies for Big Sciences including Astrophysics
Semantic Technologies for Big Sciences including AstrophysicsSemantic Technologies for Big Sciences including Astrophysics
Semantic Technologies for Big Sciences including Astrophysics
 
Semantics as a service at EMBL-EBI
Semantics as a service at EMBL-EBISemantics as a service at EMBL-EBI
Semantics as a service at EMBL-EBI
 
Big Data (SOCIOMETRIC METHODS FOR RELEVANCY ANALYSIS OF LONG TAIL SCIENCE D...
Big Data (SOCIOMETRIC METHODS FOR  RELEVANCY ANALYSIS OF LONG TAIL  SCIENCE D...Big Data (SOCIOMETRIC METHODS FOR  RELEVANCY ANALYSIS OF LONG TAIL  SCIENCE D...
Big Data (SOCIOMETRIC METHODS FOR RELEVANCY ANALYSIS OF LONG TAIL SCIENCE D...
 
Research in Intelligent Systems and Data Science at the Knowledge Media Insti...
Research in Intelligent Systems and Data Science at the Knowledge Media Insti...Research in Intelligent Systems and Data Science at the Knowledge Media Insti...
Research in Intelligent Systems and Data Science at the Knowledge Media Insti...
 
Tragedy of the (Data) Commons
Tragedy of the (Data) CommonsTragedy of the (Data) Commons
Tragedy of the (Data) Commons
 
Broad Data (India 2015)
Broad Data (India 2015)Broad Data (India 2015)
Broad Data (India 2015)
 
OpenAthens Conference 2018 - Tim Lull and Chad Smith - Cultivating your onlin...
OpenAthens Conference 2018 - Tim Lull and Chad Smith - Cultivating your onlin...OpenAthens Conference 2018 - Tim Lull and Chad Smith - Cultivating your onlin...
OpenAthens Conference 2018 - Tim Lull and Chad Smith - Cultivating your onlin...
 
Accelerating Discovery via Science Services
Accelerating Discovery via Science ServicesAccelerating Discovery via Science Services
Accelerating Discovery via Science Services
 
Found in Space: Creating and Visualizing IEEE Abstract Space for Publication ...
Found in Space: Creating and Visualizing IEEE Abstract Space for Publication ...Found in Space: Creating and Visualizing IEEE Abstract Space for Publication ...
Found in Space: Creating and Visualizing IEEE Abstract Space for Publication ...
 
Linked Open Data_mlanet13
Linked Open Data_mlanet13Linked Open Data_mlanet13
Linked Open Data_mlanet13
 
Open Data is Not Enough: Making Data Sharing Work
Open Data is Not Enough: Making Data Sharing WorkOpen Data is Not Enough: Making Data Sharing Work
Open Data is Not Enough: Making Data Sharing Work
 
we to deep learning
we to deep learning we to deep learning
we to deep learning
 
emantic web technologies and applications for Ins
emantic web technologies and applications for Insemantic web technologies and applications for Ins
emantic web technologies and applications for Ins
 
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
Being FAIR:  FAIR data and model management SSBSS 2017 Summer SchoolBeing FAIR:  FAIR data and model management SSBSS 2017 Summer School
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
 
Semantics-enhanced Geoscience Interoperability, Analytics, and Applications
Semantics-enhanced Geoscience Interoperability, Analytics, and ApplicationsSemantics-enhanced Geoscience Interoperability, Analytics, and Applications
Semantics-enhanced Geoscience Interoperability, Analytics, and Applications
 
Semantics-enhanced Cyberinfrastructure for ICMSE : Interoperability, Analyti...
Semantics-enhanced Cyberinfrastructure for ICMSE :  Interoperability, Analyti...Semantics-enhanced Cyberinfrastructure for ICMSE :  Interoperability, Analyti...
Semantics-enhanced Cyberinfrastructure for ICMSE : Interoperability, Analyti...
 
Öppen data och forskningens genomslag
Öppen data och forskningens genomslagÖppen data och forskningens genomslag
Öppen data och forskningens genomslag
 
Relationship status: Libraries and linked data in Europe
Relationship status: Libraries and linked data in EuropeRelationship status: Libraries and linked data in Europe
Relationship status: Libraries and linked data in Europe
 

Plus de Dr. Haxel Consult

AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
Dr. Haxel Consult
 
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
Dr. Haxel Consult
 
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
Dr. Haxel Consult
 

Plus de Dr. Haxel Consult (20)

AI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management
AI-SDV 2022: Henry Chang Patent Intelligence and Engineering ManagementAI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management
AI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management
 
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...
 
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
 
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...
 
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
 
AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...
AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...
AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...
 
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...
 
AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...
 
AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...
 
AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...
AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...
AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...
 
AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...
AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...
AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...
 
AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...
AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...
AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...
 
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...
 
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...
 
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
 
AI-SDV 2022: Copyright Clearance Center
AI-SDV 2022: Copyright Clearance CenterAI-SDV 2022: Copyright Clearance Center
AI-SDV 2022: Copyright Clearance Center
 
AI-SDV 2022: Lighthouse IP
AI-SDV 2022: Lighthouse IPAI-SDV 2022: Lighthouse IP
AI-SDV 2022: Lighthouse IP
 
AI-SDV 2022: New Product Introductions: CENTREDOC
AI-SDV 2022: New Product Introductions: CENTREDOCAI-SDV 2022: New Product Introductions: CENTREDOC
AI-SDV 2022: New Product Introductions: CENTREDOC
 
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...
 
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...
 

Dernier

( Pune ) VIP Baner Call Girls 🎗️ 9352988975 Sizzling | Escorts | Girls Are Re...
( Pune ) VIP Baner Call Girls 🎗️ 9352988975 Sizzling | Escorts | Girls Are Re...( Pune ) VIP Baner Call Girls 🎗️ 9352988975 Sizzling | Escorts | Girls Are Re...
( Pune ) VIP Baner Call Girls 🎗️ 9352988975 Sizzling | Escorts | Girls Are Re...
nilamkumrai
 
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
@Chandigarh #call #Girls 9053900678 @Call #Girls in @Punjab 9053900678
 
VIP Call Girls Pollachi 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Pollachi 7001035870 Whatsapp Number, 24/07 BookingVIP Call Girls Pollachi 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Pollachi 7001035870 Whatsapp Number, 24/07 Booking
dharasingh5698
 
➥🔝 7737669865 🔝▻ mehsana Call-girls in Women Seeking Men 🔝mehsana🔝 Escorts...
➥🔝 7737669865 🔝▻ mehsana Call-girls in Women Seeking Men  🔝mehsana🔝   Escorts...➥🔝 7737669865 🔝▻ mehsana Call-girls in Women Seeking Men  🔝mehsana🔝   Escorts...
➥🔝 7737669865 🔝▻ mehsana Call-girls in Women Seeking Men 🔝mehsana🔝 Escorts...
nirzagarg
 
💚😋 Bilaspur Escort Service Call Girls, 9352852248 ₹5000 To 25K With AC💚😋
💚😋 Bilaspur Escort Service Call Girls, 9352852248 ₹5000 To 25K With AC💚😋💚😋 Bilaspur Escort Service Call Girls, 9352852248 ₹5000 To 25K With AC💚😋
💚😋 Bilaspur Escort Service Call Girls, 9352852248 ₹5000 To 25K With AC💚😋
nirzagarg
 

Dernier (20)

(INDIRA) Call Girl Pune Call Now 8250077686 Pune Escorts 24x7
(INDIRA) Call Girl Pune Call Now 8250077686 Pune Escorts 24x7(INDIRA) Call Girl Pune Call Now 8250077686 Pune Escorts 24x7
(INDIRA) Call Girl Pune Call Now 8250077686 Pune Escorts 24x7
 
𓀤Call On 7877925207 𓀤 Ahmedguda Call Girls Hot Model With Sexy Bhabi Ready Fo...
𓀤Call On 7877925207 𓀤 Ahmedguda Call Girls Hot Model With Sexy Bhabi Ready Fo...𓀤Call On 7877925207 𓀤 Ahmedguda Call Girls Hot Model With Sexy Bhabi Ready Fo...
𓀤Call On 7877925207 𓀤 Ahmedguda Call Girls Hot Model With Sexy Bhabi Ready Fo...
 
( Pune ) VIP Baner Call Girls 🎗️ 9352988975 Sizzling | Escorts | Girls Are Re...
( Pune ) VIP Baner Call Girls 🎗️ 9352988975 Sizzling | Escorts | Girls Are Re...( Pune ) VIP Baner Call Girls 🎗️ 9352988975 Sizzling | Escorts | Girls Are Re...
( Pune ) VIP Baner Call Girls 🎗️ 9352988975 Sizzling | Escorts | Girls Are Re...
 
WhatsApp 📞 8448380779 ✅Call Girls In Mamura Sector 66 ( Noida)
WhatsApp 📞 8448380779 ✅Call Girls In Mamura Sector 66 ( Noida)WhatsApp 📞 8448380779 ✅Call Girls In Mamura Sector 66 ( Noida)
WhatsApp 📞 8448380779 ✅Call Girls In Mamura Sector 66 ( Noida)
 
Trump Diapers Over Dems t shirts Sweatshirt
Trump Diapers Over Dems t shirts SweatshirtTrump Diapers Over Dems t shirts Sweatshirt
Trump Diapers Over Dems t shirts Sweatshirt
 
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
 
2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs
2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs
2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs
 
Busty Desi⚡Call Girls in Vasundhara Ghaziabad >༒8448380779 Escort Service
Busty Desi⚡Call Girls in Vasundhara Ghaziabad >༒8448380779 Escort ServiceBusty Desi⚡Call Girls in Vasundhara Ghaziabad >༒8448380779 Escort Service
Busty Desi⚡Call Girls in Vasundhara Ghaziabad >༒8448380779 Escort Service
 
Call Girls Sangvi Call Me 7737669865 Budget Friendly No Advance BookingCall G...
Call Girls Sangvi Call Me 7737669865 Budget Friendly No Advance BookingCall G...Call Girls Sangvi Call Me 7737669865 Budget Friendly No Advance BookingCall G...
Call Girls Sangvi Call Me 7737669865 Budget Friendly No Advance BookingCall G...
 
VIP Call Girls Pollachi 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Pollachi 7001035870 Whatsapp Number, 24/07 BookingVIP Call Girls Pollachi 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Pollachi 7001035870 Whatsapp Number, 24/07 Booking
 
20240509 QFM015 Engineering Leadership Reading List April 2024.pdf
20240509 QFM015 Engineering Leadership Reading List April 2024.pdf20240509 QFM015 Engineering Leadership Reading List April 2024.pdf
20240509 QFM015 Engineering Leadership Reading List April 2024.pdf
 
➥🔝 7737669865 🔝▻ mehsana Call-girls in Women Seeking Men 🔝mehsana🔝 Escorts...
➥🔝 7737669865 🔝▻ mehsana Call-girls in Women Seeking Men  🔝mehsana🔝   Escorts...➥🔝 7737669865 🔝▻ mehsana Call-girls in Women Seeking Men  🔝mehsana🔝   Escorts...
➥🔝 7737669865 🔝▻ mehsana Call-girls in Women Seeking Men 🔝mehsana🔝 Escorts...
 
Al Barsha Night Partner +0567686026 Call Girls Dubai
Al Barsha Night Partner +0567686026 Call Girls  DubaiAl Barsha Night Partner +0567686026 Call Girls  Dubai
Al Barsha Night Partner +0567686026 Call Girls Dubai
 
VVIP Pune Call Girls Sinhagad WhatSapp Number 8005736733 With Elite Staff And...
VVIP Pune Call Girls Sinhagad WhatSapp Number 8005736733 With Elite Staff And...VVIP Pune Call Girls Sinhagad WhatSapp Number 8005736733 With Elite Staff And...
VVIP Pune Call Girls Sinhagad WhatSapp Number 8005736733 With Elite Staff And...
 
💚😋 Bilaspur Escort Service Call Girls, 9352852248 ₹5000 To 25K With AC💚😋
💚😋 Bilaspur Escort Service Call Girls, 9352852248 ₹5000 To 25K With AC💚😋💚😋 Bilaspur Escort Service Call Girls, 9352852248 ₹5000 To 25K With AC💚😋
💚😋 Bilaspur Escort Service Call Girls, 9352852248 ₹5000 To 25K With AC💚😋
 
Pirangut | Call Girls Pune Phone No 8005736733 Elite Escort Service Available...
Pirangut | Call Girls Pune Phone No 8005736733 Elite Escort Service Available...Pirangut | Call Girls Pune Phone No 8005736733 Elite Escort Service Available...
Pirangut | Call Girls Pune Phone No 8005736733 Elite Escort Service Available...
 
"Boost Your Digital Presence: Partner with a Leading SEO Agency"
"Boost Your Digital Presence: Partner with a Leading SEO Agency""Boost Your Digital Presence: Partner with a Leading SEO Agency"
"Boost Your Digital Presence: Partner with a Leading SEO Agency"
 
APNIC Updates presented by Paul Wilson at ARIN 53
APNIC Updates presented by Paul Wilson at ARIN 53APNIC Updates presented by Paul Wilson at ARIN 53
APNIC Updates presented by Paul Wilson at ARIN 53
 
Hire↠Young Call Girls in Tilak nagar (Delhi) ☎️ 9205541914 ☎️ Independent Esc...
Hire↠Young Call Girls in Tilak nagar (Delhi) ☎️ 9205541914 ☎️ Independent Esc...Hire↠Young Call Girls in Tilak nagar (Delhi) ☎️ 9205541914 ☎️ Independent Esc...
Hire↠Young Call Girls in Tilak nagar (Delhi) ☎️ 9205541914 ☎️ Independent Esc...
 
Ganeshkhind ! Call Girls Pune - 450+ Call Girl Cash Payment 8005736733 Neha T...
Ganeshkhind ! Call Girls Pune - 450+ Call Girl Cash Payment 8005736733 Neha T...Ganeshkhind ! Call Girls Pune - 450+ Call Girl Cash Payment 8005736733 Neha T...
Ganeshkhind ! Call Girls Pune - 450+ Call Girl Cash Payment 8005736733 Neha T...
 

II-SDV 2012 Text Mining, Term Mining and Visualization - Improving the Impact of Scholarly Publishing

  • 1. TEXT MINING, TERM MINING, AND VISUALIZATION IMPROVING THE IMPACT OF SCHOLARLY PUBLISHING MONDAY 16 APRIL 2012 NICE, FRANCE Marjorie M.K. Hlava, President Jay Ven Eman, CEO Access Innovations, Inc. mhlava@accessinn.com J_ven_eman@accessinn.com 1
  • 2. What we will cover today • Term and Text Mining • The basics of visualization • Case studies • Using subject terms as metrics • Applications • Visualizing the results
  • 3. Definitions • Term Mining - a systematic comparison processing algorithmic method to find patterns in text • Text Mining – using controlled vocabulary tags in text to find patterns and directions • Term & text mining Many similarities Can be complimentary; not mutually exclusive
  • 4. Term mining • Precise Meaningful semantic relationships; contextual Replicable; repeatable; consistent Vetted; controlled Based on a controlled vocabulary Trends; gaps; relationship analysis; visualizations Less data processing load
  • 5. Text mining Algorithmic; formulaic Neural nets, statistical, latent semantic, co - occurrence Serendipitous relationships Sentiment; hot topics; trends False drops; noise; Misleading semantic relationships Heavy processing load
  • 6. Why take a visual look? • Humans can process information 17 times faster in visual presentations • Now data can be analyzed, manipulated and presented as visual displays. • To see the trends effectively we need to make the data into rich graph-able formats 6
  • 7. Visualization of data • Needs − Measurement − Metrics − Numbers • Shows − Adjacency − Relationships − Trends − Co – occurrence − Conceptual distance • Is richer with − Linking − Semantic enrichment − Classification • Supports − Forecasting − Trend analysis − Segmentation − Distribution 7
  • 8. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Man’s attention to visual display to convey knowledge is ancient 8
  • 9. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony The art in maps is a longstanding tradition 9
  • 10. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Super imposing data is now common A mash up example 10 Traffic Injury Map UK Data Archive US National Highway Safety Administration Google Maps Base Accident categories include children automobile bicycle etc. Data time place type Source: JISC TechWatch: Data Mash-ups September 2010
  • 11. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Mash up of bird flight migrations and weather patterns http://www.youtube.com/watch?v=uPff1t4pXiI&feature=youtu.be 11
  • 12. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony http://www.youtube.com/watch?v=nokQBjk1s_8&feature=player_embedded
  • 13. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony How does it work? Develop controlled vocabulary » Prefer one with hierarchy Apply to full text » Or to the “heads” Decide on data points to convey information Divide the XML into graphable sections
  • 14. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Start with data – like this XML file 14
  • 15. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Index or tag using subject terms from thesaurus or taxonomy date, category, taxonomy term, frequency 15
  • 16. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Many views of one set of data 16
  • 17. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Load to a visualization program Like Prefuse 17
  • 18. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Or Pajek 18
  • 19. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 19
  • 20. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony National Information Center for Educational Media Albuquerque’s own » Sandia developed VxInsight » Access Innovations = NICEM Same data – several views Primary and Secondary Education in US Shows the US Valley of Science Little Science taught in elementary years 20
  • 21. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
  • 22. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony
  • 23. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Using visualization to show From a society / publisher perspective » Identify Core, Boundary and Cross Border » Provides Indicators Activity Growth Relatedness Centrality » Locates Journal domains From a thesaurus perspective » Identifies terms that are too broadly defined » Potential Improvements in thesaurus structure using topic structures 23
  • 24. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Case Study: Mapping IEEE thesaurus space We are interested in an expanded map that includes adjacencies to the IEEE data » Expanded term set shows adjacent white space; opportunities for expansion Overlaps and edges of the science » We need comparison data Learn the directions in the field » Low occurrence rate in IEEE documents? » Linkage to terms in IEEE documents? Where do we find these terms? How can we add them? 24
  • 25. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony The process Built a rule base to auto index IEEE content » “90 % accuracy out of the box on journal data”* » “80% out of the box on proceedings data”* The overlapping data sets » Auto indexed 1.2 million Xplore records » Auto indexed 10 years of US Patent data » Auto indexed 10 years of Medline Term sets used » IEEE thesaurus terms rule base » Medical Subject Headings (MeSH) (and simple rule base) » Defense Technical Information Center (DTIC) Thesaurus ( and simple rule base) » Similar level of detail to current IEEE thesaurus terms 25
  • 26. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Defining expanded term space 26 IEEE 2kterms 1.2M documents 1. The data - Select related corpus 14kDTIC 475k patents 24kMeSH PubMed 525k docs
  • 27. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Defining expanded term space 27 IEEE 2kterms 1.2M documents 2. Identify related terms Use the IEEE Thesaurus to index the three collections
  • 28. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Defining expanded term space 28 IEEE 2kterms 1.2M documents 2. Identify related terms Use MESH and DTIC to also index the three collections
  • 29. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 29 IEEE 2kterms 1.2M documents 3. Resulting term set The co-indexed items from the three collections Defining expanded term space
  • 30. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 30 4. Term:Term Matrix Where do the articles and their indexing intersect? Defining expanded term space
  • 31. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 31 Visualization Strategies Matrix Visualization Software
  • 32. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 32 All data up-posted to the top level
  • 33. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 33 Many map options IEEE ExperiencePrevious Experience
  • 34. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 34 Sensors Council Nucl Plasma Sci Soc Nanotech Council Ultrason, Ferro … Prod Saf Engng Soc Oceanic Engng Soc Geosci Rem Sens Soc Council Supercond Compon, Packag … Instr Measur Soc Magnetics Soc Dielectr El Insul Soc Electromag Compat Soc Antennas Propag Soc Power Electron Soc Electron Dev Soc Circuits & Systems Power & Energy Soc Industry Appl Soc Solid St Circuits Soc Industr Electr Soc Microwave Theory Soc Aerosp Electr Sys Soc Sys Man Cyber Society Computer Intelligence Society Systems Council Reliability Society Education Society Prof Commun Society Computer Society Robot Autom Soc Social Impl Techn Council Electr Design Auto Signal Proc Soc Intell Transp Sys Soc Commun Soc Info Theory Soc Vehicular Techn Soc Consumer Electr Soc Broadcast Techn Soc Photonics Soc Eng Med Biol Sci IEEE Portfolio
  • 35. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 35 Radial Visualization
  • 36. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 36 Publication Strategy JASIST reference
  • 37. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 37 Conference Strategy
  • 38. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony 38 Turbines Measurement Circuits Amplifiers Displays Games Toys Flow Cooling Heating Components Gearing Brakes Dynamics Vehicles, Parts Disk Optics Photochem Molding Conductors Coatings Lasers Lamps Motors Plants, Micro-orgs Control Boats Oilfield Services Med Instruments Welding Conveyers Rubber Acyclic Comp Footwear Lubricants Radiology Catalysis Macromolecules Sprayers Electrochem Fitness Hygiene Cleaning Printing Paper IC Engines Magn/Elect Magnets Textiles Layers Medical Devices Clocks Pipes Valves Blasting Cables Appliances Outerwear Exhaust Pumps Packaging Aircraft Semiconductors Use a Thesaurus to Label Maps Agriculture Food Consumer Products Construction Automotive + Defense Industrial Products Leisure Energy Telecom Computer HW/SW Electronics Chemicals Pharma Metals Health Care
  • 39. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Questions Answered Is there a way, using our own information, to forecast our direction? Where is the industry headed? What about by technology sector? Does our coverage match our mission and vision? Can we become smarter about our data and potential markets using our collection in new ways? Are the societies publishing and talking about what their charter indicates they cover? What are the trends – are topics emerging/cooling? Can we use technology and our own data to explore these questions while enhancing our data? 39
  • 40. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony The research team Access Innovations / Data Harmony » Founded in 1978 » Data enrichment and normalization » Suite of Semantic Enrichment tools SciTechStrategies » Understanding data through visualization IEEE Indexing & Abstracting Group 40
  • 41. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony We looked at visualization of data Finding the Metrics » Measurement » Numbers » Terms as indicators Ways to show » Adjacency » Relationships » Trends » Co – occurrence » Conceptual distance How to enrich with » Linking » Semantic enrichment » Classification Maps supporting » Forecasting » Trend analysis » Segmentation » Distribution 41
  • 42. Well Formed Data • Semantic Enrichment • Taxonomies • Access Innovations • Data Harmony Effective maps require Contextual data Detailed data Classification methods At least two directions in the matrix A little art for fun 42
  • 43. 43 It just takes a little imagination Thank you Marjorie M.K. Hlava President mhlava@accessinn.com Jay Ven Eman, CEO J_ven_eman@accessinn.com , Access Innovations 505-998-0800