DocumentCode
1145686
Title
AI and Global Science and Technology Assessment
Author
Hsinchun Chen
Author_Institution
Univ. of Arizona, Tucson, AR, USA
Volume
24
Issue
4
fYear
2009
Firstpage
68
Lastpage
88
Abstract
Addressing the research opportunities we´ve identified could substantially broaden the spectrum of multilingual text-mining and its practicality for supporting global S&T knowledge management. These opportunities also share a common set of challenges that deserve further attention. For example, competitive intelligence surveillance, which allows organizations to understand their current and potential competitors better, often requires the extraction of names of different organizations, technologies, or products from various S&T documents. When dealing with multilingual documents, adequate cross-lingual entity-resolution mechanisms are essential for effective global S&T analysis. Furthermore, some S&T documents are scientific or technologically oriented, whereas others have a predominantly business orientation. This increases the chance of different documents using different terms inreferring to identical or similar concepts. Establishing cross-domain interoperability is essential, especially in multilingual environments.
Keywords
Internet; citation analysis; competitive intelligence; data mining; knowledge management; natural language processing; text analysis; AI; Web-based platform; business orientation; citation analysis; competitive intelligence surveillance; cross-domain interoperability; cross-lingual entity-resolution mechanism; global S&T knowledge management; global science-and-technology assessment; multilingual document analysis; multilingual text-mining; Artificial intelligence; Chaos; Investments; Knowledge management; Nanotechnology; Productivity; Publishing; Software libraries; Visualization; Artificial intelligence; research; social issues;
fLanguage
English
Journal_Title
Intelligent Systems, IEEE
Publisher
ieee
ISSN
1541-1672
Type
jour
DOI
10.1109/MIS.2009.68
Filename
5172891
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