A Knowledge Acquisition Method for Event Extraction and Coding Based on Deep Patterns

2018 ◽  
pp. 16-31
Author(s):  
Alfred Krzywicki ◽  
Wayne Wobcke ◽  
Michael Bain ◽  
Susanne Schmeidl ◽  
Bradford Heap
Keyword(s):  
Knowledge Acquisition ◽  
Event Extraction ◽  
Acquisition Method

The Incremental Knowledge Acquisition Based on Hash Algorithm

2016 ◽  
Vol 24 (03) ◽  
pp. 347-366 ◽  
Author(s):  
Qing-Hua Zhang ◽  
Long-Yang Yao ◽  
Guan-Sheng Zhang ◽  
Yu-Ke Xin
Keyword(s):  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Algorithm Analysis ◽  
Data Sets ◽  
Hash Algorithm ◽  
Acquisition Method

In this paper, a new incremental knowledge acquisition method is proposed based on rough set theory, decision tree and granular computing. In order to effectively process dynamic data, describing the data by rough set theory, computing equivalence classes and calculating positive region with hash algorithm are analyzed respectively at first. Then, attribute reduction, value reduction and the extraction of rule set by hash algorithm are completed efficiently. Finally, for each new additional data, the incremental knowledge acquisition method is proposed and used to update the original rules. Both algorithm analysis and experiments show that for processing the dynamic information systems, compared with the traditional algorithms and the incremental knowledge acquisition algorithms based on granular computing, the time complexity of the proposed algorithm is lower due to the efficiency of hash algorithm and also this algorithm is more effective when it is used to deal with the huge data sets.

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

2019 ◽  
Author(s):  
Maqbool Hussain ◽  
Muhammad Afzal ◽  
Khalid M. Malik ◽  
Taqdir Ali ◽  
Wajahat Ali Khan ◽  
...  
Keyword(s):  
Oral Cavity ◽  
Knowledge Acquisition ◽  
Formal Verification ◽  
Medical Knowledge ◽  
Knowledge Model ◽  
Validation Process ◽  
Hybrid Knowledge ◽  
Verification Process ◽  
Acquisition Method

Validation and verification are the critical requirements in the knowledge acquisition method for the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Therefore, it is required to enhance a hybrid knowledge acquisition method that thwarts the inconsistencies using formal verification. This paper presents the verification process using the Z formal method and its outcome as an enhanced acquisition method – known as the refined knowledge acquisition (ReKA) method. The ReKA method adopted verification method and explored the mechanism of theorem proving using the Z notation. It enables to identify inconsistencies in the validation process used for hybrid knowledge acquisition. Additionally, it refines the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. Consequently, ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. The criteria ensure the validity of final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the addition of criteria by ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57\% compared to a similar approach with an accuracy of 69.7\%. Furthermore, ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. In conclusion, ReKA is formally proved method which always yields valid knowledge model having high quality, supporting local practices, and influenced from standard guidelines.

A Rough-Fuzzy RBF Neural Network Based on PSO Algorithm

2013 ◽  
Vol 710 ◽  
pp. 617-622
Author(s):  
Jing Zhao
Keyword(s):  
Neural Network ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Training Sample ◽  
Network Training ◽  
Acquisition Method

A Rough-Fuzzy RBF Neural Network was raised based on PSO Algorithm. In this model,gives a knowledge acquisition method that based on rough set theory,the Rough-Fuzzy RBF neural network are constructed according to the results of the knowledge acquisition,the PSO are used to optimize the network parameters.This paper take number plate for example to conduct a simulation experiment.The results shows that the model can simplify the network training sample,optimize the network structure and enhance the systems study efficiency and the precision.

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