International Journal of Man-Machine Studies - Knowledge acquisition for knowledge-based systems, part 1. Based on an AAAI work
MOLE: a tenacious knowledge-acquisition tool
International Journal of Man-Machine Studies - Knowledge acquisition for knowledge-based systems, part 1. Based on an AAAI work
An overview of knowledge-acquisition and transfer
International Journal of Man-Machine Studies
Automatic knowledge base refinement for classification systems
Artificial Intelligence
KITTEN: knowledge initiation and transfer tools for experts and novices
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Knowledge acquisition: principles and guidelines
Knowledge acquisition: principles and guidelines
EMCUD: a knowledge acquisition method which captures embedded meanings under uncertainty
International Journal of Man-Machine Studies
KADS: a modelling approach to knowledge engineering
Knowledge Acquisition - Special issue on the KADS approach to knowledge engineering
Automatic Knowledge Acquisition from Subject Matter Experts
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
Acquiring domain knowledge for negotiating agents: a case of study
International Journal of Human-Computer Studies
A novel self-optimizing approach for knowledge acquisition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Evaluating organization external knowledge acquisition
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
KAMET II: KAMET plus knowledge generation
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
The KAMET II methodology: Knowledge acquisition, knowledge modeling and knowledge generation
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Due to the knowledge explosion, the new objects will be evolved in a dynamic environment. Hence, the knowledge can be classified into static knowledge and dynamic knowledge. Although many knowledge acquisition methodologies, based upon the Repertory Grid technique, have been proposed to systematically elicit useful rules from static grid from domain experts, they lack the ability of grid evolution to incrementally acquire the dynamic knowledge of new evolved objects. In this paper, we propose dynamic EMCUD, a new Repertory Grid-based knowledge acquisition methodology to elicit the embedded meanings of knowledge (embedded rules bearing on m objects and k object attributes), to enhance the ability of original EMCUD to iteratively integrate new evolved objects and new added attributes into the original Acquisition Table (AT) and original Attribute Ordering Table (AOT). The AOT records the relative importance of all attribute to each object in EMCUD to capture the embedded meanings with acceptable certainty factor value by relaxing or ignoring some minor attributes. In order to discover the new evolved objects, a collaborative framework including local knowledge based systems (KBSs) and a collaborative KBS is proposed to analyze the correlations of inference behaviors of embedded rules between multiple KBSs in a dynamic environment. Each KBS monitors the frequent inference behaviors of interesting embedded rules to construct a small AT increment to facilitate the acquisition of dynamic knowledge after experts confirming the new evolved objects. Moreover, the significance of knowledge may change after a period of time, a trend of all attributes to each evolved object is used to construct a new AOT increment to help experts automatically adjust the relative importance of each attribute to each object using time series analysis approach. Besides, three cases are considered to assist experts in adjusting the certainty factor values of the dynamic knowledge of the new evolved objects from the collection of inference logs in the collaborative KBS. To evaluate the performance of dynamic EMCUD in incrementally integrating new knowledge into the knowledge base, a worm detection prototype system is implemented.