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Artificial Intelligence Laboratory

Knowledge Mapping for Nanotechnology

Research Goal

Nanotechnology holds the promise of revolutionizing a wide range of application areas and has been recognized by most countries as critical to a nation's future technology competence. Increased funding and research have generated a growing number of patents and an increase in the amount of literature available. Government interest, funding levels, industry investment, and active research activities have created a greater need to monitor global development of Nanoscale Science and Engineering (NSE) and the R&D status in industry and academic institutions, and to understand the impact of the funding in the NSE field.

The research directions of the Artificial Intelligence Lab include:

  1. Development of the patent analysis framework for worldwide NSE R&D status.
  2. In-depth grant-patent association analysis to assess the impact of NSF funding and NSF-funded researchers in NSE field.
  3. Comparative study of the NSE patents issued by multiple patent offices.
  4. Assessment of NSE research in academia using scientific literature and comparison with industries' efforts as represented by patents.
  5. Citation network topological analysis for knowledge diffusion trend identification.
  6. Text mining based methods for research topic identification and topic burst identification.
  7. Development of an integrated NanoMap system for NSE patent and literature retrieval, analysis, and visualization.

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Funding

Funding for this research was received from the following sources:

NSF-#0738803 December 1, 2007 – May 30, 2009 ($100,000)
"SGER: Inter-repository Patent Analysis to Understand Worldwide Nanotechnology Research and Development"
 
NSF-#0654232 December 15, 2006 – May 30, 2008 ($100,000)
"Worldwide Nanotechnology Development: A Comparative Study of Global Patents"
 
NSF-#0533749 August 15, 2005-July 31, 2007 ($200,000)
"NanoMap: Mapping Nanotechnology Development"
 
NSF-#0549663 September 15, 2005-August 31, 2006 ($100,000)
"Mapping Nanotechnology Development Based on the ISI Literature-Citation Database"
 
NSF- #0311652 May 1, 2003-April 30, 2005 ($99,935)
"Intelligent Patent Analysis for Nanoscale Science and Engineering"
 
NSF- #0311628 May 15, 2003-August 31, 2004 ($99,935)
"Intelligent Patent Analysis and Visualization"
 
NSF- #0204375 January 1, 2002-December 31, 2002 ($99,980)
"NanoPort: Intelligent Web Searching for Nanoscale Science and Engineering"
 
NSF- #9817473 April 1999 - March 2002 ($500,000)
Digital Library Initiative 2, "High-performance Digital Library Systems: From Information Retrieval to Knowledge Management"
 

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Acknowledgements

We would like to acknowledge the generous support provided by Thomson Scientific.

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Approach & Methodology

Testbed:

  • US Patent and Trademark Office (USPTO) NSE patent data (1976-2005) searched by a domain expert, who provided keywords on: title, abstract, description, claims, and specifications.
  • European Patent Office (EPO) NSE patent data (1978-2004) searched by domain expert who provided keywords on title and abstract.
  • Japan Patent Office (JPO) NSE patent data (1976-2004) searched by domain expert who provided keywords on title and abstract.
  • National Science Foundation (NSF) NSE grant data (1989-2004) searched by domain expert who provided keywords on : grant title, program, and abstract.
  • Thomson Science Citation Index Expanded (SCIE) NSE literature data (1976-2004) searched by domain expert who provided keywords on title, paper keywords, and abstract.

Techniques:

  • Basic bibliographic analysis on the key indicators of technology development performance
  • Content map analysis
    1) Using the Arizona Noun Phraser-indexing and Kohonen self-organization map (SOM) algorithm to compare and associate the main topic areas.
    2) Longitudinal content map analysis visualizes the change of topic areas in terms of time periods.
  • Citation network analysis
    1) Network visualization: Graphviz, an open source graph drawing software, provided by AT&T Labs (Gansner and North, 2000) (available at: http://www.research.att.com/sw/tools/graphviz/).
    2) Network visualization: NetDraw, a free network drawing program by Steve Borgatti (available at: http://www.analytictech.com/downloadnd.htm)
    3) Network topological analysis: Using the social network analysis method to study the patterns in citation networks, the topological measures of the citation networks and their implications.
  • Statistical Hypothesis Testing Statistically compare the difference of comparative groups in terms of the publication significance and impact on the field.

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Team Members

   Dr. Hsinchun Chen hchen@eller.arizona.edu
   Xin Li xinli@email.arizona.edu
   Daning Hu (Josh) hud@email.arizona.edu
   Yan Dang (Mandy) ydang@email.arizona.edu
   Cathy Larson cal@eller.arizona.edu

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Publications

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