Abstract:
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 than with Scirus, a leading search engine for scientific articles. Through our prototype system, we demonstrated the feasibility of using such an integrated approach and the study brought insight into applying the proposed domain-independent architecture to different areas of science and engineering in the future. © 2006 Elsevier B.V. All rights reserved.
Abstract:
Border and transportation security is a critical part of the Department of Homeland Security's (DHS) national strategy. DHS strategy calls for the creation of "smart borders" where information from local, state, federal, and international sources can be combined to support risk-based management tools for border-management agencies. This paper proposes a framework for effectively integrating such data to create cross-jurisdictional Criminal Activity Networks (CAN)s. Using the approach outlined in the framework, we created a CAN system as part of the DHS-funded BorderSafe project This paper describes the system, reports on feedback received from investigating officers, and highlights key issues and challenges.
Abstract:
The five essays on global science and technology S&T assessment from distinguished experts in knowledge mapping, scientometrics, information visualization, digital libraries, and multilingual knowledge management has been discussed. The first essay, 'China S&T Assessment' proposes three fundamental S&T assessment metrics and shows the Chinese emphasis on the physical and engineering sciences and its significant research productivity gains. The another essay, 'Open Data and Open Code for S&T Assessment', introduces science maps to help humans mentally organize, access, and manage complex digital library collections. The essay, 'Global S&T Assessment by Analysis of Large ETD Collections introduce the highly successful Networked Digital Library of Theses and Dissertations (NDLTD) project. The final essay, 'Managing Multilingual S&T Knowledge' describes a research framework for cross-lingual and polylingual text categorization and category integration.
Abstract:
Opinion mining which is a sub discipline within data mining and computational linguistics refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online news sources, social media comments, and other user-generated content is discussed. Frameworks and methods for integrating sentiments and opinions expressed with other computational representations such as interesting topics or product features extracted from user-generated text, participant reply networks, spikes and outbreaks of ideas or events are also critically needed. Disagreement and subjectivity also held significant relationships with volatility, where less disagreement and high levels of subjectivity predicted periods of high stock volatility. Positive sentiment reduces trading volume, perhaps because satisfied shareholders hold their stock, while negative sentiment induces trading activity as shareholders defect.
