Learning Concept Hierarchy from Short Texts Using Context Coherence

2018 ◽  
pp. 319-329 ◽  
Author(s):  
Abdulqader Almars ◽  
Xue Li ◽  
Ibrahim A. Ibrahim ◽  
Xin Zhao
Keyword(s):  
Concept Hierarchy ◽  
Learning Concept

scholarly journals Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

2021 ◽  
Author(s):  
Bryar A. Hassan ◽  
Tarik A. Rashid ◽  
Seyedali Mirjalili
Keyword(s):  
Clustering Algorithm ◽  
Concept Lattice ◽  
Formal Context ◽  
Formal Concept ◽  
Context Analysis ◽  
Concept Hierarchy ◽  
Evolutionary Clustering ◽  
Learning Concept ◽  
Concept Hierarchies

AbstractIt is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA); (2) A framework to decrease the formal context’s ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.

Learning Concept Hierarchy from Folksonomy

2011 ◽  
Author(s):  
Shubin Cai ◽  
Heng Sun ◽  
Sishan Gu ◽  
Zhong Ming
Keyword(s):  
Concept Hierarchy ◽  
Learning Concept

A hybrid approach for learning concept hierarchy from Malay text using artificial immune network

2010 ◽  
Vol 10 (1) ◽  
pp. 275-304 ◽  
Author(s):  
Mohd Zakree Ahmad Nazri ◽  
Siti Mariyam Shamsuddin ◽  
Azuraliza Abu Bakar ◽  
Salwani Abdullah
Keyword(s):  
Hybrid Approach ◽  
Immune Network ◽  
Artificial Immune ◽  
Concept Hierarchy ◽  
Artificial Immune Network ◽  
Learning Concept

A Hybrid Approach for Learning Concept Hierarchy from Malay Text Using GAHC and Immune Network

2009 ◽  
pp. 315-328
Author(s):  
Mohd Zakree Ahmad Nazri ◽  
Siti Mariyam Shamsuddin ◽  
Azuraliza Abu Bakar ◽  
Salwani Abdullah
Keyword(s):  
Hybrid Approach ◽  
Immune Network ◽  
Concept Hierarchy ◽  
Learning Concept

Paradigma Pendidikan Kritis: Menuju Humanisasi Pendidikan

1970 ◽  
Vol 13 (1) ◽  
pp. 110-115
Author(s):  
Sunhaji Sunhaji
Keyword(s):  
Process Approach ◽  
Critical Education ◽  
Human Right ◽  
National History ◽  
Student Centered ◽  
Self Identity ◽  
Education Model ◽  
Learning Concept ◽  
Education Process

The process of education must apply with “Learning Process Skill”, not “Learning Concept”. Process approach marked with student centered curricula, not teacher centered. Role of teacher is as facilitator, mediator, dynamizing, organizing, and catalyst to apply “dialog” as spirit of education process. Critical education model is an education that independent from internal-institutional fetter, social hegemony, or structured to maintain political and economical stability. These happen in the length of our national history, then produce tame-weak human accorded to system condition. Whereas, education is human right, even people right to enhance its maturity, self-identity, and independence to serve his function to his God. .

Concept Hierarchy Extraction from Textbooks

2015 ◽  
Author(s):  
Shuting Wang ◽  
Chen Liang ◽  
Zhaohui Wu ◽  
Kyle Williams ◽  
Bart Pursel ◽  
...  
Keyword(s):  
Concept Hierarchy

Machine learning techniques based on security management in smart cities using robots

Work ◽  
2021 ◽  
pp. 1-12
Author(s):  
Zhang Mengqi ◽  
Wang Xi ◽  
V.E. Sathishkumar ◽  
V. Sivakumar
Keyword(s):  
Machine Learning ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Security Management ◽  
Machine Learning Techniques ◽  
Quality Of Services ◽  
Learning Techniques ◽  
Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

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