Hsinchun Chen

Hsinchun Chen

Professor, Management Information Systems
Regents Professor
Member of the Graduate Faculty
Professor, BIO5 Institute
Primary Department
Contact
(520) 621-4153

Research Interest

Dr Chen's areas of expertise include:Security informatics, security big data; smart and connected health, health analytics; data, text, web mining.Digital library, intelligent information retrieval, automatic categorization and classification, machine learning for IR, large-scale information analysis and visualization.Internet resource discovery, digital libraries, IR for large-scale scientific and business databases, customized IR, multilingual IR.Knowledge-based systems design, knowledge discovery in databases, hypertext systems, machine learning, neural networks computing, genetic algorithms, simulated annealing.Cognitive modeling, human-computer interactions, IR behaviors, human problem-solving process.

Publications

Chen, H. (2003). Special issue: "Web retrieval and mining". Decision Support Systems, 35(1), 1-5.
Chung, W., Lai, G., Bonillas, A., Elhourani, T., Tseng, T. (., & Chen, H. (2005). Building web directories in different languages for decision support: A semi-automatic approach. Association for Information Systems - 11th Americas Conference on Information Systems, AMCIS 2005: A Conference on a Human Scale, 1, 467-475.

Abstract:

Web directories organize voluminous information into hierarchical structures, helping users to quickly locate relevant information and to support decision-making. The development of existing Web directories either relies on expert participation that may not be available or uses automatic approaches that lack precision. As more users access the Web in their native languages, better approaches to organizing and developing non-English Web directories are needed. In this paper, we have proposed a semi-automatic approach to building domain-specific Web directories in different languages by combining human precision and machine efficiency. Using the approach, we have built Web directories in the Spanish business (SBiz) and Arabic medical (AMed) domains. Experimental results show that the SBiz and AMed directories achieved significantly better recall, F value, and satisfaction rating than benchmark directories. These encouraging results show that the approach can be used to build high-quality Web directories to sup ort decision-making.

Chen, H., Houston, A. L., Sewell, R. R., & Schatz, B. R. (1998). Internet browsing and searching: User evaluations of category map and concept space techniques. Journal of the American Society for Information Science, 49(7), 582-603.

Abstract:

The Internet provides an exceptional testbed for developing algorithms that can improve browsing and searching large information spaces. Browsing and searching tasks are susceptible to problems of information overload and vocabulary differences. Much of the current research is aimed at the development and refinement of algorithms to improve browsing and searching by addressing these problems. Our research was focused on discovering whether two of the algorithms our research group has developed, a Kohonen algorithm category map for browsing, and an automatically generated concept space algorithm for searching, can help improve browsing and/or searching the Internet. Our results indicate that a Kohonen self-organizing map (SOM)-based algorithm can successfully categorize a large and eclectic Internet information space (the Entertainment subcategory of Yahoo!) into manageable sub-spaces that users can successfully navigate to locate a homepage of interest to them. The SOM algorithm worked best with browsing tasks that were very broad, and in which subjects skipped around between categories. Subjects especially liked the visual and graphical aspects of the map. Subjects who tried to do a directed search, and those that wanted to use the more familiar mental models (alphabetic or hierarchical organization) for browsing, found that the map did not work well. The results from the concept space experiment were especially encouraging. There were no significant differences among the precision measures for the set of documents identified by subject-suggested terms, thesaurus-suggested terms, and the combination of subject- and thesaurus-suggested terms. The recall measures indicated that the combination of subject- and thesaurus-suggested terms exhibited significantly better recall than subject-suggested terms alone. Furthermore, analysis of the homepages indicated that there was limited overlap between the homepages retrieved by the subject-suggested and thesaurus-suggested terms. Since the retrieved homepages for the most part were different, this suggests that a user can enhance a keyword-based search by using an automatically generated concept space. Subjects especially liked the level of control that they could exert over the search, and the fact that the terms suggested by the thesaurus were "real" (i.e., originating in the homepages) and therefore guaranteed to have retrieval success.

Zhu, B., & Chen, H. (2002). Visualizing the archive of a computer mediated communication process. Proceedings of the ACM International Conference on Digital Libraries, 385-.

Abstract:

The archive of computer-mediated communication (CMC) process contains knowledge shared and information about participants' behavior patterns. However, most CMC systems focus only on organizing the content of discussions. We propose to demo a prototype system that integrates a social visualization technique with existing information analysis technologies to graphically summarize both the content and behavior of a CMC process.

Chen, H., Zhou, Y., Reid, E. F., & Larson, C. A. (2011). Introduction to special issue on terrorism informatics. Information Systems Frontiers, 13(1), 1-3.