Exploiting affinity propagation for automatic acquisition of domain concept in ontology learning

2011 ◽  
Author(s):  
Iqbal Qasim ◽  
Jin-Woo Jeong ◽  
Sharifullah Khan ◽  
Dong-Ho Lee
Keyword(s):  
Affinity Propagation ◽  
Ontology Learning ◽  
Automatic Acquisition ◽  
Domain Concept

Domain Concept Filtering Method Based on Word Embedding and Statistics

2014 ◽  
Vol 644-650 ◽  
pp. 1935-1938 ◽  
Author(s):  
Ying Li ◽  
Shi Chao Cui ◽  
Zhi Sheng Lv ◽  
Yong Bin Wang
Keyword(s):  
Low Frequency ◽  
Word Embedding ◽  
Ontology Learning ◽  
Accuracy Rate ◽  
Semantic Level ◽  
Filtering Method ◽  
Domain Concept

On the basis of summarizing the concept filtering methods in the current Ontology learning, a method of domain concept filtering in the semantic level based on combination of word embedding and conventional statistics was presented, which can identify low-frequency words well, and as far as possible to ensure universality. Through experimental contrast, the proposed approach was proved to have a higher accuracy rate than the ways based on statistics.

Automatic acquisition of customer reviews

2009 ◽  
Vol 29 (3) ◽  
pp. 846-848 ◽  
Author(s):  
Yong-wen HUANG ◽  
Zhong-shi HE ◽  
Xing WU
Keyword(s):  
Customer Reviews ◽  
Automatic Acquisition

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

Sign in / Sign up

Export Citation Format

Share Document