Construction Search Structural Materials Based on Formal Concept Analysis and Association Rule Mining

2012 ◽  
Author(s):  
Ruihui Mu
Keyword(s):  
Association Rule ◽  
Formal Concept Analysis ◽  
Association Rule Mining ◽  
Structural Materials ◽  
Concept Analysis ◽  
Formal Concept ◽  
Rule Mining

scholarly journals Construction Search Engine Based on Formal Concept Analysis and Association Rule Mining

2012 ◽  
Vol 6-7 ◽  
pp. 625-630 ◽  
Author(s):  
Hong Sheng Xu
Keyword(s):  
Search Engine ◽  
Association Rule ◽  
Formal Concept Analysis ◽  
Association Rule Mining ◽  
High Efficiency ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Frequent Itemsets ◽  
Formal Concept ◽  
Rule Mining

In the form of background in the form of concept partial relation to the corresponding concept lattice, concept lattice is the core data structure of formal concept analysis. Association rule mining process includes two phases: first find all the frequent itemsets in data collection, Second it is by these frequent itemsets to generate association rules. This paper analyzes the association rule mining algorithms, such as Apriori and FP-Growth. The paper presents the construction search engine based on formal concept analysis and association rule mining. Experimental results show that the proposed algorithm has high efficiency.

scholarly journals Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?

2020 ◽  
Vol 34 (3) ◽  
pp. 317-327 ◽  
Author(s):  
Ana Ozaki
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Description Logic ◽  
Inductive Logic ◽  
Computational Learning Theory ◽  
Formal Concept ◽  
Ontology Learning ◽  
Formal Representation ◽  
Rule Mining ◽  
Diverse Field

Abstract The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.

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