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

Chau, M., Chen, H., Qin, J., Zhou, Y., Qin, Y., Sung, W., & McDonald, D. (2002). Comparison of two approaches to building a vertical search tool: A case study in the nanotechnology domain. Proceedings of the ACM International Conference on Digital Libraries, 135-144.

Abstract:

As the Web has been growing exponentially, it has become increasingly difficult to search for desired information. In recent years, many domain-specific (vertical) search tools have been developed to serve the information needs of specific fields. This paper describes two approaches to building a domain-specific search tool. We report our experience in building two different tools in the nanotechnology domain - (1) a server-side search engine, and (2) a client-side search agent. The designs of the two search systems are presented and discussed, and their strengths and weaknesses are compared. Some future research directions are also discussed.

Roussinov, D. G., & Chen, H. (2001). Information navigation on the web by clustering and summarizing query results. Information Processing and Management, 37(6), 789-816.

Abstract:

We report our experience with a novel approach to interactive information seeking that is grounded in the idea of summarizing query results through automated document clustering. We went through a complete system development and evaluation cycle: designing the algorithms and interface for our prototype, implementing them and testing with human users. Our prototype acted as an intermediate layer between the user and a commercial Internet search engine (Alta Vista), thus allowing searches of the significant portion of World Wide Web. In our final evaluation, we processed data from 36 users and concluded that our prototype improved search performance over using the same search engine (AltaVista) directly. We also analyzed effects of various related demographic and task related parameters. © 2001 Elsevier Science Ltd.

Chen, H. (2011). Editorial: Design science, grand challenges, and societal impacts. ACM Transactions on Management Information Systems, 2(1).
Chen, H., & Wang, F. (2005). Artificial intelligence for homeland security. IEEE Intelligent Systems, 20(5), 12-16.
Abbasi, A., & Chen, H. (2008). Cybergate: A design framework and system for text analysis of computer-mediated communication. MIS Quarterly: Management Information Systems, 32(4), 811-837.

Abstract:

Content analysis of computer-mediated communication (CMC) is important for evaluating the effectiveness of electronic communication in various organizational settings. CMC text analysis relies on systems capable of providing suitable navigation and knowledge discovery functionalities. However, existing CMC systems focus on structural features, with little support for features derived from message text. This deficiency is attributable to the informational richness and representational complexities associated with CMC text. In order to address this shortcoming, we propose a design framework for CMC text analysis systems. Grounded in systemic functional linguistic theory, the proposed framework advocates the development of systems capable of representing the rich array of information types inherent in CMC text. It also provides guidelines regarding the choice of features, feature selection, and visualization techniques that CMC text analysis systems should employ. The CyberGate system was developed as an instantiation of the design framework. CyberGate incorporates a rich feature set and complementary feature selection and visualization methods, including the writeprints and ink blots techniques. An application example was used to illustrate the system 's ability to discern important patterns in CMC text. Furthermore, results from numerous experiments conducted in comparison with benchmark methods confirmed the viability of CyberGate 's features and techniques. The results revealed that the CyberGate system and its underlying design framework can dramatically improve CMC text analysis capabilities over those provided by existing systems.