scholarly journals Neural Network-Based Tree Translation for Knowledge Base Construction

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 38706-38717
Author(s):  
Haijun Zhang
Keyword(s):  
Neural Network ◽  
Knowledge Base ◽  
Knowledge Base Construction

A convolutional neural network-based model for knowledge base completion and its application to search personalization

Semantic Web ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 947-960 ◽  
Author(s):  
Dai Quoc Nguyen ◽  
Dat Quoc Nguyen ◽  
Tu Dinh Nguyen ◽  
Dinh Phung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Knowledge Base

scholarly journals Taxonomy Extraction for Customer Service Knowledge Base Construction

2019 ◽  
pp. 175-190
Author(s):  
Bianca Pereira ◽  
Cecile Robin ◽  
Tobias Daudert ◽  
John P. McCrae ◽  
Pranab Mohanty ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Customer Service ◽  
Knowledge Base Construction

scholarly journals Knowledge Base Completion with Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

2018 ◽  
Vol 33 (2) ◽  
pp. F-H72_1-10 ◽  
Author(s):  
Takuo Hamaguchi ◽  
Hidekazu Oiwa ◽  
Masashi Shimbo ◽  
Yuji Matsumoto
Keyword(s):  
Neural Network ◽  
Knowledge Base ◽  
Network Approach ◽  
Neural Network Approach

Cognition of a Robotic Manipulator Using the Q-Learning Based Situation-Operator Model

2018 ◽  
Vol 11 (1) ◽  
pp. 146-157 ◽  
Author(s):  
Akash Dutt Dubey ◽  
Ravi Bhushan Mishra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Path Planning ◽  
Mobile Robot ◽  
Knowledge Base ◽  
Operator Model ◽  
Q Learning ◽  
Sensory Inputs ◽  
Learning Module ◽  
Som Model

In this article, we have applied cognition on robot using Q-learning based situation operator model. The situation operator model takes the initial situation of the mobile robot and applies a set of operators in order to move the robot to the destination. The initial situation of the mobile robot is defined by a set of characteristics inferred by the sensor inputs. The Situation-Operator Model (SOM) model comprises of a planning and learning module which uses certain heuristics for learning through the mobile robot and a knowledge base which stored the experiences of the mobile robot. The control and learning of the robot is done using q-learning. A camera sensor and an ultrasonic sensor were used as the sensory inputs for the mobile robot. These sensory inputs are used to define the initial situation, which is then used in the learning module to apply the valid operator. The results obtained by the proposed method were compared to the result obtained by Reinforcement-Based Artificial Neural Network for path planning.

scholarly journals Effective supra-classifiers for knowledge base construction

1999 ◽  
Vol 20 (11-13) ◽  
pp. 1347-1352 ◽  
Author(s):  
Kurt D Bollacker ◽  
Joydeep Ghosh
Keyword(s):  
Knowledge Base ◽  
Knowledge Base Construction

Construction and Functions of Self-Study System for Power Plant Operation

2000 ◽  
Author(s):  
Shengli Tang ◽  
Zuwei He ◽  
Tao Chang ◽  
Liming Xuan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Power Plant ◽  
Knowledge Base ◽  
Practical Experience ◽  
Plant Operation ◽  
Self Study ◽  
Power Plant Operation ◽  
Artificial Neural

Abstract In this paper, the Construction and functions of the self-study system for power plant operation is introduced. As a self-study system, it consists of two parts, a simulator and knowledge base. The knowledge base has been built by the combination of expert system and artificial neural network, which supports the system with practical experience and theoretic knowledge. The trainees’ knowledge can be improved by using the system. The realization of the intelligent training function, applications of expert system and artificial neural network are mainly introduced in this paper.

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