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Table 1. Knowledge Components EXTERNAL INTERNAL

in Informing Science Special Series: Informing Each Other Volume 6, 2003 Using the World Wide Web to Connect Research and Professional Practice: Towards Evidence-Based Practice
by Daniel L. Moody
"... In PAGE 7: ... Knowledge Content The knowledge content of the system was based on the requirements identified in the pre-implementation sur- vey. It consists of five major components, which correspond to different classifications of knowledge on the tacit/explicit dimension and the internal/external dimension (whether the knowledge was produced inside or out- side the organisation), as shown in Table1 . Unlike most knowledge management systems, which primarily focus on internal knowledge (Sveiby, 1997; Davenport et al, 1998; Davenport and Prusak, 1998; Hansen et al, 1999), this system is mainly focused on providing access to external knowledge, and in particular, the results of medical research.... ..."

Table 1: The efiect of using external knowledge

in Search and Retrieval — relevance feedback, search process
by Andrei Broder, Marcus Fontoura, Evgeniy Gabrilovich, Amruta Joshi, Vanja Josifovski, Tong Zhang
"... In PAGE 5: ... We employed two major US search engines, and used their results in two ways, either only summaries or the full text of crawled result pages. Figure 2 and Table1 show that such extra knowledge con- siderably improves classiflcation accuracy. Interestingly, we found that search engine A performs consistently better with full-page text, while search engine B performs better when... ..."

Table 4: Overall results using no external knowledge

in A Simple Named Entity Extractor using AdaBoost
by Xavier Carreras Llu, Xavier Carreras, Llu S M Arquez, Llu S Padr O 2003
Cited by 9

Table 3: Overall results using no external knowledge

in A Simple Named Entity Extractor using AdaBoost
by Xavier Carreras And, Xavier Carreras, Llu S M Arquez, Llu S Padr O 2003
Cited by 9

Table 3: Overall results using no external knowledge

in unknown title
by unknown authors

Table 1: Internal (To The Analyst) and External Knowledge Requirements For Approval Decision

in IT-Based Knowledge Management To Support Organizational Learning IT-Based Knowledge Management To Support Organizational Learning: Visa Application Screening At The INS
by Susan Gasson, Katherine M. Shelfer
"... In PAGE 13: ... The data collection approach is summarized in Table 1. Table1 . Data Collection and Analysis Approach Data collection method Subjects Objective Initial interviews Senior INS Law-Enforcement Manager; Two senior managers from prime contractor for IT at INS Understand stages of visa approval processes; Determine key criteria for decision Interactive group workshop sessions and ad hoc interviews with analysts at a workshop to discuss border control 24 analysts from INS; 12 analysts from two friendly country law enforcement agencies Validate process model for visa approval; Determine key intelligence questions faced by analysts Small group and individual interviews to explore the risk management stages shown in Figure 2 24 analysts from INS; 12 analysts from friendly country law enforcement agencies Explore decision-processes used to answer the key intelligence questions Telephone and email interviews 2 senior managers from prime IT vendor; 2 senior INS law-enforcement managers; 10 Visa approval analysts at different levels of experience and seniority.... ..."

Table 1 presents a summary of the opposition between the kuhnian and the lakato- sian epistemologies applied to the emergence of agent-orientation.

in Multi-Agent Spiral Software Engineering: A Lakatosian Approach
by Christophe Schinckus, Yves Wautelet, Manuel Kolp, Facultés Universitaires
"... In PAGE 13: ... Commensurability between two research programs due to the unchanged hard core. Table1 . kuhnian and lakatosian visions to the emergence of the agent-orientation.... ..."

Table 2: The External Analysis Method

in Improving Agent Learning through Rule Analysis
by Cristina Boicu, Gheorghe Tecuci, Mihai Boicu 2005
"... In PAGE 4: ... This threshold value depends on the application domain, and can be statistically determined. The external analysis method is presented in Table2 . It computes the number of solutions generated by the rule in the knowledge base.... ..."
Cited by 2

Table 1: Taxonomy of knowledge types for VE presentations (per Munro et al 2002). IRVEs are relatively new and the effectiveness of various information and interaction designs are not yet known. Nonetheless, we believe giving users a unified interactive experience of some process or phenomenon can improve learning, performance, and the accuracy of their mental models. While IRVEs provide the potential for users to integrate heterogeneous information from diverse sources in one session, most IRVEs are simplistic consisting of worlds with animations and labels or linked applications in external windows. Few exploit the full design space to give users flexible control over their views and interactions, but promising work is emerging in academia and industry that deserves mention.

in Desktop InformationRich Virtual Environments: Challenges and Techniques. Virtual Reality 8(1
by Nicholas F. Polys, Doug A. Bowman, Chris North 2004
"... In PAGE 2: ... 2.0 Related Work Munro et al [2002] outlined the cognitive processing issues in virtual environments by the type of information they convey ( Table1 ). In reviewing VE presentations and tutoring systems, the authors note that VEs are especially appropriate for: navigation and locomotion in complex environments, manipulation of complex objects ad devices in 3D space, learning abstract concepts with spatial characteristics, complex data analysis, and decision making.... ..."
Cited by 1

Table 1: External model partitions:

in Distributed Reflective Architectures
by Catriona M. Kennedy, Supervisor Prof, Aaron Sloman, Prof John Barnden, Dr. William Edmondson
"... In PAGE 26: ...Table1 0: Baby apos;s e ectors: E ector function EE(B; W ) move-to (new position) collect treasure EE(B; N) take control from N EI(B; B) none at present EI(B; N) none at present Table 11: Nursemaid apos;s e ectors: E ector function EE(N; W ) move-to (new position) recharge vehicle EE(N; B) take control from B EI(N; N) repair own rulesystem EI(N; B) repair B apos;s rulesystem Notes: (1) SE(i; j) are sensors of Aj apos;s e ects on the external environment. Note that energy level, interest etc.... In PAGE 26: ...E ector function EE(B; W ) move-to (new position) collect treasure EE(B; N) take control from N EI(B; B) none at present EI(B; N) none at present Table1 1: Nursemaid apos;s e ectors: E ector function EE(N; W ) move-to (new position) recharge vehicle EE(N; B) take control from B EI(N; N) repair own rulesystem EI(N; B) repair B apos;s rulesystem Notes: (1) SE(i; j) are sensors of Aj apos;s e ects on the external environment. Note that energy level, interest etc.... ..."
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