[German title: Decision Support Systems, 43 (1), 2007 – interessante Artikel]
Decision Support Systems
Volume 43, Issue 1 , February 2007, Pages 141-151
Mobile Commerce: Strategies, Technologies, and Applications
Could the use of a knowledge-based system lead to implicit learning?
Solomon Antony and Radhika Santhanam
Abstract: The primary objective of a knowledge-based system (KBS) is to use stored knowledge to provide support for decision-making activities. Empirical studies identify improvements in decision processes and outcomes with the use of such knowledge-based systems. This research suggests that though a KBS is primarily developed to help users in their decision-making activities, as an unintentional consequence, it may induce them to implicitly learn more about a problem. Implicit learning occurs when a person learns unconsciously or unintentionally, without being explicitly instructed or tutored. To test these ideas, a laboratory-based experiment was conducted with a KBS that could provide support for data modeling activities. Results indicated support for implicit learning because subjects who interacted with the KBS exhibited better knowledge on data modeling concepts than those who did not interact with the KBS. Two versions of the KBS were tested, one with a restrictive interface and the other with a guidance interface, and both versions of the interface supported implicit learning. Implications for future research on the design and development of KBSs are proposed.
Keywords: Knowledge-based systems; Learning; Interface; Data modeling
Reference metadata extraction using a hierarchical knowledge representation framework
Min-Yuh Day, Richard Tzong-Han Tsai, Cheng-Lung Sung, Chiu-Chen Hsieh, Cheng-Wei Lee, Shih-Hung Wu, Kun-Pin Wu, Chorng-Shyong Ong and Wen-Lian Hsu
Abstract: The integration of bibliographical information on scholarly publications available on the Internet is an important task in the academic community. Accurate reference metadata extraction from such publications is essential for the integration of metadata from heterogeneous reference sources. In this paper, we propose a hierarchical template-based reference metadata extraction method for scholarly publications. We adopt a hierarchical knowledge representation framework called INFOMAP, which automatically extracts metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference styles with a high degree of precision. The overall average accuracy is 92.39% for the six major reference styles compared in this study.
Keywords: Reference extraction; Metadata extraction; Knowledge representation framework; INFOMAP
Dare to share: Protecting sensitive knowledge with data sanitization
Abstract: Data sanitization is a process that is used to promote sharing of transactional databases among organizations while alleviating concerns of individual organizations by preserving confidentiality of their sensitive knowledge in the form of sensitive association rules. It hides the frequent itemsets corresponding to the sensitive association rules that contain sensitive knowledge by modifying the sensitive transactions that contain those itemsets. This process is guided by the need to minimize the impact on the data utility of the sanitized database by allowing mining as much as possible of the non-sensitive knowledge in the form non-sensitive association rules from the sanitized database. We propose three heuristic approaches for the sanitization problem. Results from extensive tests conducted on publicly available real datasets indicate that the approaches are effective and outperform a previous approach in terms of data utility at the expense of computational speed. The proposed approaches sanitize also the databases with great data accuracy, thus resulting in little distortion of the released databases. We recommend that the database owner sanitize the database using the third proposed hybrid approach.
Keywords: Data mining; Sensitive knowledge protection; Data sanitization; Data utility
© 2007 Elsevier B.V. All rights reserved.