AI and Global Science and Technology Assessment
Hsinchun Chen, Ronald Neil Kostoff, Chaomei Chen, Jian Zhang, Michael S. Vogeley, Katy Börner, Nianli Ma, Russell J. Duhon, Angela M. Zoss, Venkat Ramanan Srinivasan, Edward A. Fox, Christopher C. Yang, Chih‐Ping Wei · IEEE Intelligent Systems · 2009
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.