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
Knowledge acquisition driven by constructive and interactive induction
EKAW'92 Proceedings of the 6th European knowledge acquisition workshop on Current developments in knowledge acquisition
KADS: a modelling approach to knowledge engineering
Knowledge Acquisition - Special issue on the KADS approach to knowledge engineering
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An integrated environment for knowledge acquisition
Proceedings of the 6th international conference on Intelligent user interfaces
An intelligent distributed environment for active learning
Journal on Educational Resources in Computing (JERIC)
EVA: an interactive web-based collaborative learning environment
Computers & Education - Special section on multimedia in engineering education
Generalized Version Space Learning Algorithm for Noisy and Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
The Knowledge Acquisition and Representation Language, KARL
IEEE Transactions on Knowledge and Data Engineering
Building a Large Knowledge Base from a Structured Source
IEEE Intelligent Systems
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Creating Semantic Web Contents with Protégé-2000
IEEE Intelligent Systems
Machine Learning
Automatic Ontology-Based Knowledge Extraction from Web Documents
IEEE Intelligent Systems
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
A New Mechanism of Mining Network Behavior
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Open Mind Common Sense: Knowledge Acquisition from the General Public
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Using JessTab to Integrate Protégé and Jess
IEEE Intelligent Systems
Recent worms: a survey and trends
Proceedings of the 2003 ACM workshop on Rapid malcode
A framework for abstracting data sources having heterogeneous representation formats
Data & Knowledge Engineering
The Knowledge Engineering Review
Acquiring domain knowledge for negotiating agents: a case of study
International Journal of Human-Computer Studies
Knowledge analysis on process models
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Intelligent tutoring system for electric circuit exercising
IEEE Transactions on Education
Development of a tribological failure knowledge model
International Journal of Knowledge Engineering and Data Mining
Expert Systems with Applications: An International Journal
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 | 12.07 |
Many knowledge acquisition methodologies have been proposed to elicit rules systematically with embedded meaning from domain experts. But, none of these methods discusses the issue of discovering new modified objects in a traditional classification knowledge based system. For experts to sense the occurrence of new variants and revise the original rule base, to collect sufficient relevant information becomes increasingly important in the knowledge acquisition field. In this paper, the method variant objects discovering knowledge acquisition (VODKA) we proposed includes three stages (log collecting stage, knowledge learning stage, and knowledge polishing stage) to facilitate the acquisition of new inference rules for a classification knowledge based system. The originality of VODKA is to identify these new modified objects, the variants, from the way that the existing knowledge based system fails in applying some rules with low certainty degree. In this method, we try to classify the current new evolving object identified according to its attributes and their corresponding values. According to the analysis of the collected inference logs, one of the three recommendations (including adding a new attribute-value of an attribute, modifying the data type of an attribute, or adding a new attribute) will be suggested to help experts observe and characterize the new confirmed variants. VODKA requires E-EMCUD (extended embedded meaning capturing and uncertainty deciding). EMCUD is a knowledge acquisition system which relies on the repertory grids technique to manage object/attribute-values tables and to produce inferences rules from these tables. The E-EMCUD we used here is a new version of EMCUD to update existing tables by adding new objects or new attributes and to adapt the original embedded rules. Here, a computer worm detection prototype is implemented to evaluate the effectiveness of VODKA. The experimental results show that new worm variants could be discovered from inference logs to customize the corresponding detection rules for computer worms. Moreover, VODKA can be applied to the e-learning area to learn the variant learning behaviors of students and to reconstruct the teaching materials in improving the performance of e-learners.