this paper, we propose a generic and integrated approach to searching and browsing the Internet in a multilingual world. Based on this approach, we have developed the Chinese Business Intelligence Portal (CBizPort) , a meta-search engine that searches for business information of mainland China, Taiwan, and Hong Kong. Additional functions provided by CBizPort include encoding conversion (between Simplified Chinese and Traditional Chinese), summarization, and categorization. Experimental results of our user evaluation study show that the searching and browsing performance of CBizPort was comparable to that of regional Chinese search engines, and CBizPort could significantly augment these search engines. Subjects' verbal comments indicate that CBizPort performed best in terms of analysis functions, cross-regional searching, and user-friendliness, whereas regional search engines were more efficient and more popular. Subjects especially liked CBizPort's summarizer and categorizer, which helped in understanding search results. These encouraging results suggest a promising future of our approach to Internet searching and browsing in a multilingual world

As huge amounts of knowledge are created rapidly, effective information access becomes an important issue. Especially for critical domains, such as medical and financial areas, efficient retrieval of concise and relevant information is highly desired. In this paper we propose a new user query based text summarization technique that makes use of Unified Medical Language System, an ontology knowledge source from National Library of Medicine. We compare our method with keyword-only approach, and our ontologybased method performs clearly better. Our method also shows potential to be used in other information retrieval areas.

Abstract. This paper reports our research in the Web page filtering process in specialized search engine development. We propose a machine-learning-based approach that combines Web content analysis and Web structure analysis. Instead of a bag of words, each Web page is represented by a set of contentbased and link-based features, which can be used as the input for various machine learning algorithms. The proposed approach was implemented using both a feedforward/backpropagation neural network and a support vector machine. An evaluation study was conducted and showed that the proposed approaches performed better than the benchmark approaches. 1

In this chapter we provide a broad overview of selected knowledge management, data mining, and text mining techniques and their use in various emerging biomedical applications. It aims to set the context for subsequent chapters. We first introduce five major paradigms for machine learning and data analysis including: probabilistic and statistical models, symbolic learning and rule induction, neural networks, evolution-based algorithms, and analytic learning and fuzzy logic. We also discuss their relevance and potential for biomedical research. Example applications of relevant knowledge management, data mining, and text mining research are then reviewed in order including: ontologies; knowledge management for health care, biomedical literature, heterogeneous databases, information visualization, and multimedia databases; and data and text mining for health care, literature, and biological data. We conclude the paper with discussions of privacy and confidentiality issues of relevance to biomedical data mining.

Abstract—Neural networks have been used in various applications on the World Wide Web, but most of them only rely on the available input-output examples without incorporating Webspecific knowledge, such as Web link analysis, into the network design. In this paper, we propose a new approach in which the Web is modeled as an asymmetric Hopfield Net. Each neuron in the network represents a Web page, and the connections between neurons represent the hyperlinks between Web pages. Web content analysis and Web link analysis are also incorporated into the model by adding a page content score function and a link score function into the weights of the neurons and the synapses, respectively. A simulation study was conducted to compare the proposed model with traditional Web search algorithms, namely, a breadth-first search and a best-first search using PageRank as the heuristic. The results showed that the proposed model performed more efficiently and effectively in searching for domain-specific Web pages. We believe that the model can also be useful in other Web applications such as Web page clustering and search result ranking. Index Terms—Hopfield net, neural network, spreading activation, Web analysis, Web mining.

While the Web provides a lot of useful information to managers and decision makers in organizations for decision support, it requires a lot of time and cognitive effort for users to sift through a search result list returned by search engines to find useful information. Previous research in information visualization has shown that visualization techniques can help users comprehend information and accomplish information tasks more efficiently and effectively. However, only a limited number of such techniques have been applied to Web search result visualization with mixed evaluation results. Using a design science approach, this research designed and implemented a glyph (a graphical object that represents the values of multiple dimensions using multiple visual parameters) and a system for visualizing Web search results. A flower metaphor was adopted in the glyph design to represent the characteristics and metadata of Web documents. Following the cognitive fit theory, an experimental study was conducted to evaluate three displays: a numeric display, a glyph display, and a combined display which showed numbers only, glyphs only, and both, respectively. Experimental results showed that the glyph display and the combined display performed better when task complexity was high, and the numeric display and the combined display performed better when task complexity was low. The combined display also received the best perceived usability from the subjects. Based on the findings, the implications of the study to research and practice are discussed and some future research directions are suggested.

As huge amounts of knowledge are created rapidly, effective information access becomes an important issue. Especially for critical domains, such as medical and financial areas, efficient retrieval of concise and relevant information is highly desired. In this paper we propose a new user query based text summarization technique that makes use of WordNet, a general knowledge source from Princeton University. Our summarization system is specially tuned to summarize medical documents by integrating Unified Medical Language System, a medical ontology knowledge source

There has been a tremendous growth in the amount of information and resources on the World Wide Web that are useful to researchers and practitioners in science domains. While the Web has made the communication and sharing of research ideas and results among scientists easier and faster than ever, its dynamic and unstructured nature also makes the scientists faced with such problems as information overload, vocabulary difference, and lack of analysis tools. To address these problems, it is highly desirable to have an integrated, “one-stop shopping ” Web portal to support effective information searching and analysis as well as to enhance communication and collaboration among researchers in various scientific fields. In this paper, we review existing information retrieval techniques and related literature, and propose a framework for developing integrated Web portals that support information searching and analysis for scientific knowledge. Our framework incorporates collection building, meta-searching, keyword suggestion, and various content analysis techniques such as document summarization, document clustering, and topic map visualization. Patent analysis techniques such as citation analysis and content map analysis are also incorporated. To demonstrate the feasibility of our approach, we developed based on our architecture a knowledge portal, called NanoPort, in the field of nanoscale science and engineering. We report our experience and explore the various issues of relevance to developing a Web portal for scientific domains. The system was compared to other search systems in the field and several design issues were identified. An evaluation study was conducted and the results showed that subjects were more satisfied with the NanoPort system

journal homepage: www.elsevier.com/locate/dss

With increasing emphasis on using data analysis in decision support in healthcare organizations, we describe an example in collecting and analyzing data for a health organization. In particular, we describe the process in identifying the best location for Hong Kong Red Cross Blood Transfusion Service to establish a new blood donation center based on past donation data. Our analysis is divided into two parts. The first part focuses on data analysis which studies the context of data itself. We have identified the relationship among donor’s age, gender, blood type and frequency of donation, the relationship between donation frequencies and distance from the donation center, the characteristic of different donation center, and the trend of blood donation. The second part focuses on new center analysis which applies the findings derived from the first part in