Construction of a Knowledge Base for Empirical Knowledge in Neurosurgery

Author(s):  
Ayuki Joto ◽  
Takahiro Fuchi ◽  
Hiroshi Noborio ◽  
Katsuhiko Onishi ◽  
Masahiro Nonaka ◽  
...  
1992 ◽  
Vol 01 (04) ◽  
pp. 563-595
Author(s):  
ENG LIAN LIM ◽  
JOHN McCALLUM ◽  
KWOK HUNG CHAN

Knowledge acquisition is tedious and error-prone. Consequently, a knowledge base may be inconsistent, and contains unreachable rules, redundant rules, and rules which may lead to deadends and infinite loops. There are three approaches for checking these anomalies: interactive, non-interactive pairwise and non-interactive pathwise. In this article, we will present a graph theoretical model called Production-graph for checking knowledge base anomalies along the non-interactive pathwise approach. Production-graph uses graph theoretical constructions to represent facts and rules, as well as relevant properties of the knowledge base that leads to anomalies. Distinctive features of Production-graph include: (i) Using Production-graph, we are able to check on groups of problem instances rather than on individual problem instances. This eliminates the problem of having infinitely many problem instances. (ii) Empirical knowledge is used to limit the problem instances to practically realizable problems. (iii) Effects of chaining both rules and facts are considered.


Evaluation ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. 237-258 ◽  
Author(s):  
Peter Milley ◽  
Barbara Szijarto ◽  
Kate Svensson ◽  
J. Bradley Cousins

Social innovation has gained prominence as a way to address social problems and needs. Evaluators and social innovators are conceptualizing and implementing evaluation approaches for social innovation contexts; however, no systematic effort has yet been made to explore and assess the overlap between evaluation and social innovation based on the empirical knowledge base. We address this gap, drawing on 28 empirical studies of evaluation in social innovation contexts to describe what evaluation practices look like, what drives those practices, and how they affect social innovations. Findings indicate most had developmental purposes, emphasized collaborative approaches, and used multiple methods. Prominent drivers were a complexity perspective, a learning-oriented focus, and the need for responsiveness. Reported influences on social innovations included advancing strategies, improving delivery, balancing aggregate and local information needs, and reducing risk. Conflict resolution, the quality of relationships, and availability of time and capacity mediated these influences. More peer-reviewed empirical studies and a broader range of study designs are needed, including research on how evaluations influence social innovation processes over time, phases, space and scale.


1987 ◽  
Vol 18 (4) ◽  
pp. 38-40 ◽  
Author(s):  
Brian Bolton

Emphasizing that a cardinal characteristic of the professions is an empirical knowledge base, three methodological strategies are recommended: (1) use of standard outcome instrumentation and longitudinal designs, (2) use of the multivariate experimental principle in conjunction with case studies, and (3) involvement of rehabilitation practitioners as participants as well as collaborators in research projects. Moreover, it is argued that practitioners have some responsibility to contribute to the expansion of knowledge, in addition to their obligation to assist in research activities. Finally, detailed criticism of the rules of professional conduct comprising Canon 8 is given.


2013 ◽  
Vol 6 (4) ◽  
pp. 101-126 ◽  
Author(s):  
Mahbub Rashid

OBJECTIVE: To help designers and researchers and other proponents of evidence-based design (EBD) overcome limitations concerning knowledge categorization and acquisition of evidence-based design (EBD). BACKGROUND: The evidence-based design (EBD) approach for healthcare facilities has been widely embraced by both designers and researchers in recent years; however, there are some limitations concerning knowledge categorization and acquisition of EBD. These limitations include an overemphasis on empirical knowledge gained by experimental research, a narrow focus that excludes design knowledge generated outside healthcare and allied fields, and a lack of interest in empirical knowledge gained by qualitative studies. In order to overcome these limitations, the proponents of EBD must acknowledge that design knowledge relevant to healthcare design can be found in disciplines unrelated to healthcare; that design knowledge does not always need empirical validation; and that design knowledge of the semantic kind can be more easily accessed and understood through qualitative studies. CONCLUSIONS: To reassess the foundations of knowledge of EBD with moderated skepticism is necessary because there are philosophical and analytical problems yet to be overcome in delivering on the promises of EBD. To question and reassess the foundations of knowledge base of EBD is not necessarily to deny its value, but rather to stimulate a judicious and balanced appraisal of its limitations so that, in future, we are able to take necessary steps to overcome these limitations.


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