PMID: 18263007;Abstract:
An automatic indexing and concept classification approach to a multilingual (Chinese and English) bibliographic database is presented. We introduced a multi-linear term-phrasing technique to extract concept descriptors (terms or keywords) from a Chinese-English bibliographic database. A concept space of related descriptors was then generated using a co-occurrence analysis technique. Like a man-made thesaurus, the system-generated concept space can be used to generate additional semantically-relevant terms for search. For concept classification and clustering, a variant of a Hopfield neural network was developed to cluster similar concept descriptors and to generate a small number of concept groups to represent (summarize) the subject matter of the database. The concept space approach to information classification and retrieval has been adopted by the authors in other scientific databases and business applications, but multilingual information retrieval presents a unique challenge. This research reports our experiment on multilingual databases. Our system was initially developed in the MS-DOS environment, running ETEN Chinese operating system. For performance reasons, it was then tested on a UNIX-based system. Due to the unique ideographic nature of the Chinese language, a Chinese term-phrase indexing paradigm considering the ideographic characteristics of Chinese was developed as a multilingual information classification model. By applying the neural network based concept classification technique, the model presents a novel way of organizing unstructured multilingual information. © 1996 IEEE.
Abstract:
In a relatively short interval for an emerging technology, nanotechnology has made a significant economic impact in numerous sectors including semiconductor manufacturing, catalysts, medicine, agriculture, and energy production. A part of the United States (US) government investment in basic research has been realized in the last two decades through the National Science Foundation (NSF), beginning with the nanoparticle research initiative in 1991 and continuing with support from the National Nanotechnology Initiative after fiscal year 2001. This paper has two main goals: (a) present a longitudinal analysis of the global nanotechnology development as reflected in the United States Patent and Trade Office (USPTO) patents and Web of Science (WoS) publications in nanoscale science and engineering (NSE) for the interval 1991-2012; and (b) identify the effect of basic research funded by NSF on both indicators. The interval has been separated into three parts for comparison purposes: 1991-2000, 2001-2010, and 2011-2012. The global trends of patents and scientific publications are presented. Bibliometric analysis, topic analysis, and citation network analysis methods are used to rank countries, institutions, technology subfields, and inventors contributing to nanotechnology development. We then, examined how these entities were affected by NSF funding and how they evolved over the past two decades. Results show that dedicated NSF funding used to support nanotechnology R&D was followed by an increased number of relevant patents and scientific publications, a greater diversity of technology topics, and a significant increase of citations. The NSF played important roles in the inventor community and served as a major contributor to numerous nanotechnology subfields. © 2013 Springer Science+Business Media.
Abstract:
Expertise management systems are being widely adopted in organizations to manage tacit knowledge. These systems have successfully applied many information technologies developed for document management to support collection, processing, and distribution of expertise information. In this paper, we report a study on the potential of applying visualization techniques to support more effective and efficient exploration of the expertise information space. We implemented two widely applied dimensionality reduction visualization techniques, the self-organizing map (SOM) and multidimensional scaling (MDS), to generate compact but distorted (due to the dimensionality reduction) map visualizations for an expertise data set. We tested cognitive fit theory in our context by comparing the SOM and MDS displays with a standard table display for five tasks selected from a low-level, domain-independent visual task taxonomy. The experimental results based on a survey data set of research expertise of the business school professors suggested that using both SOM and MDS visualizations is more efficient than using the table display for the associate, compare, distinguish, and cluster tasks, but not the rank task. Users generally achieved comparable effectiveness for all tasks using the tabular and map displays in our study. © 2006 Elsevier B.V. All rights reserved.
Abstract:
The basic building block of a multilingual information retrieval system is the input system. Chinese and Japanese characters pose great challenges for the conventional 101-key alphabet-based keyboard, because they are radical-based and number in the thousands. This paper reviews the development of various approaches and then presents a framework and working demonstrations of Chinese and Japanese input methods implemented in Java, which allow open deployment over the web to any platform. The demo includes both popular keyboard input methods and neural network handwriting recognition using a mouse or pen. This framework is able to accommodate future extension to other input mediums and languages of interest.
