Formal Concept Analysis Supporting Ontology Learning From Database

Ouyang, Chunping; Liu, Yongbin

doi:10.1166/asl.2012.2725

Cited by 2 publications

(2 citation statements)

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“…A node of concept lattices is an objects/attributes pair, called a formal concept, consisting of two parts: the extent (objects the concept covers) and intent (attributes describing the concept). Concept lattices have already been applied to a wide range of disciplines such as knowledge discovery (Belohlavek et al, 2014;Berghammer and Winter, 2013;Huchard et al, 2007;Jiang and Deogun, 2007;Lei et al, 2009;Missaoui et al, 2012;Poelmans et al, 2010), information retrieval, software engineering (Jay et al, 2008;Tilley and Eklund, 2007), rough set theory (Jiang et al, 2010;Qu et al, 2007;Yao, 2004;Wei and Qi, 2010;Zhou and Yao, 2010), knowledge ontology (Ge et al, 2012;Chunping and Liu, 2012) and the connections with description logics (Bazin and Ganascia, 2012;Ma et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Concept Lattices in Green Farmland Databases and Concept Intent Reduction for the Mass Food Production

Lei¹,

Tian²

2016

AJFST

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Formal Concept Analysis (FCA) in green farmland databases and Concept Intent Reduction for the mass food production provides a method for extracting concepts from binary contexts. However, FCA-concepts cannot describe negations and disjunctions of attributes. Hence, we take the logic operators into consideration in the process of constructing concepts and obtain new extended concepts, which are more expressive than FCA-concepts. This study mainly discusses the connections between FCA-concepts and concepts with logic values in green farmland databases and concept intent reduction for the mass food production and provides a method for reducing concepts. The reduction does not lose essential information. Results can be used in data mining and construction of architecture ontology.

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Section: Introductionmentioning

confidence: 99%

Concept Lattices in Green Farmland Databases and Concept Intent Reduction for the Mass Food Production

Lei¹,

Tian²

2016

AJFST

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“…FCA [6] is an effective data analysis technique, which automatically generates hierarchies called concept lattices from contexts. Recently, concept lattices have already been successfully applied to a wide range of scientific disciplines such as knowledge discovery [1, 3, 4, 5, 8-11, 15, 18], information retrieval [2,13,16,19], software engineering [12,20], rough set theory [17,21,23,25], and knowledge ontology [7,14].…”

Section: Introductionmentioning

confidence: 99%

The Maximal Relation Based on A Given Relation Schema and Its Concept Lattice

Lei¹,

Sui²,

Tian³

2013

JSW

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Formal concept analysis (FCA) is a valid tool for data mining and knowledge discovery,
which identifies concept lattices from binary relations. Given a nonempty finite set $A$ of binary attributes, one obtains a maximal binary relation R^max based on a relation schema S(A). Firstly, we analyze concepts in R^max and the concept lattice L(R^max, and there are some important results as follows: for any two concepts in R^max, the union of their intents is an intent of some concept in R^max, and further the intent of their supremum is the union of their intents;
for any two concepts in R^max, if one of them is not a sub-concept or super-concept of the other one, then the union of their extents is not an extent of any concept in R^max; L(R^max) is a complemented distributive lattice. Secondly, we provide the structural connection between L(R) and L(R^max: for any relation R based on S(A), there is a supremum-preserving order-embedding map from L(R) to L(R^max), and conversely, there is an infimum-preserving order-preserving map from L(R^max) to L(R), which is generally not a surjective homomorphism.
Thirdly, we propose two algorithms to extract concepts in R from L(R^max), which are respectively based on intents and extents of concepts, and prove their soundness. These results have already been used to analyze the data in architectual engineering and medical science

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Formal Concept Analysis Supporting Ontology Learning From Database

Cited by 2 publications

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Concept Lattices in Green Farmland Databases and Concept Intent Reduction for the Mass Food Production

Concept Lattices in Green Farmland Databases and Concept Intent Reduction for the Mass Food Production

The Maximal Relation Based on A Given Relation Schema and Its Concept Lattice

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