Applying the JBOS reduction method for relevant knowledge extraction

Dias, Sérgio M.; Vieira, Natália Gomes

doi:10.1016/j.eswa.2012.10.010

Cited by 28 publications

(16 citation statements)

References 27 publications

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“…• The selection of important concepts (Babin & Kuznetsov, 2012;Bělohlávek & Vychodil, 2005a;Bělohlávek & Macko, 2011;Bělohlávek & Trnecka, 2012;Dias & Viera, 2013;Li et al, 2013;Zhang et al, 2012). • Reducing the size of the concept lattice (Bělohlávek & Vychodil, 2005a;Elloumi et al, 2004;Kang et al, 2012;Kumar et al, 2015;Singh & Kumar, 2012a, 2012b2014b) and reducing the storage complexity (Gajdos & Snásel, 2014 …”

Section: Discussionmentioning

confidence: 98%

“…Several methods have been proposed for reducing the size of the concept lattice (Kang, Li, Wang, & Qu, 2012;Kumar & Srinivas, 2010;Singh & Kumar, 2014b). For the reduction of the concept lattice, some researchers have focused on defining the weight of given formal concepts (Bělohlávek & Macko, 2011;Dias & Viera, 2013;Li et al, 2013). Most of the available approaches are based on selection of the concepts through specific measuring of their importance in FCA in a crisp setting.…”

Section: Discussionmentioning

confidence: 98%

“…To measure the changes between the obtained formal concepts, some methods have recently Downloaded by [New York University] at 21:30 30 May 2015 been proposed based on crisply generated concepts (Bělohlávek et al, 2005), stability (Babin & Kuznetsov, 2012), basic level (Bělohlávek & Trnecka, 2012), fidelity (Dias & Viera, 2013), and Levenshtein distance (Martin et al, 2013). To achieve this aim, the current paper considered crisply generated concepts, i.e., considering attributes as a crisp set B c ⊆ P such that A = (B c ) ↓ (and thus B = (B c ) ↓↑ ) as discussed by Bělohlávek et al (2005).…”

Section: Empirical Analysismentioning

confidence: 99%

“…In recent years researchers have concentrated on the selection of formal concepts using their weight (Babin & Kuznetsov, 2012;Bělohlávek & Macko, 2011;Bělohlávek & Trnecka, 2012) at different granulations (Li, Mei, Xu, & Qian, 2015) of their weight (Dias & Viera, 2013;Li, He, & Zhu, 2013;Zhang, Guo, Zhang, Wang, & Pedrycz, 2012), such that the changes between fuzzy concept lattices can be measured (Martin, Rahim, & Mazidian, 2013). Each of the available approaches focuses on reducing the number of formal concepts based on finding similar concepts, in contrast to measuring the uncertainty and the storage complexity.…”

Section: Introductionmentioning

confidence: 98%

See 3 more Smart Citations

Fuzzy concept lattice reduction using Shannon entropy and Huffman coding

Singh

Gani

2015

Journal of Applied Non-Classical Logics

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In the last decade, formal concept analysis (FCA) in a fuzzy setting has received more attention for knowledge processing tasks in various fields. The hierarchical order visualisation of generated formal concepts is a major concern for the practical application of FCA. In this process, a major issue is the huge number of formal concepts generated from 'a large context', and another problem is their 'storage' complexity. To deal with these issues a method is proposed in this paper based on Shannon entropy and Huffman coding. The proposed method is illustrated using crisply generated concepts such that the changes between obtained concepts can be measured using Levenshtein distance. The analysis derived from the proposed method is illustrated with an example for FCA in a fuzzy setting.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 98%

Section: Empirical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Fuzzy concept lattice reduction using Shannon entropy and Huffman coding

Singh

Gani

2015

Journal of Applied Non-Classical Logics

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“…Currently, many kinds of concepts [18,34,45,46] have been developed to meet different requirements in the real world. It should be pointed out that these concepts have been applied in many fields such as rough analysis, rule acquisition and knowledge discovery [2,4,5,12,16,19,25,27,28,38]. Moreover, granular computing [21-24, 41, 44, 48, 49] has been incorporated into concept learning to improve the learning efficiency [10,20,40].…”

Section: Introductionmentioning

confidence: 99%

Cognitive concept learning from incomplete information

Zhao

Liu

et al. 2016

Int. J. Mach. Learn. & Cyber.

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Cognitive concept learning is to learn concepts from a given clue by simulating human thought processes including perception, attention and thinking. In recent years, it has attracted much attention from the communities of formal concept analysis, cognitive computing and granular computing. However, the classical cognitive concept learning approaches are not suitable for incomplete information. Motivated by this problem, this study mainly focuses on cognitive concept learning from incomplete information. Specifically, we put forward a pair of approximate cognitive operators to derive concepts from incomplete information. Then, we propose an approximate cognitive computing system to perform the transformation between granular concepts as incomplete information is updated periodically. Moreover, cognitive processes are simulated based on three types of similarities. Finally, numerical experiments are conducted to evaluate the proposed cognitive concept learning methods.

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Ontology-Informed Lattice Reduction Using the Discrimination Power Index

Quboa

Behnaz

Mehandjiev

et al. 2019

Graph-Based Representation and Reasoning

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The increasing reliance on data for decision making has led to a number of techniques for automatic knowledge acquisition such as Formal Concept Analysis (FCA). FCA creates a lattice comprising partial order relationships between sets of object instances in a domain (extent) and their properties (intent). This is mapped onto a semantic knowledge structure comprising domain concepts with their instances and properties. However, this automatic extraction of structure from a large number of instances usually leads to a lattice which is too complex for practical use. Algorithms to reduce the lattice exist. However, these mainly rely on the lattice structure and are agnostic about any prior knowledge about the domain. In contrast, this paper uses existing domain knowledge encoded in a semantic ontology and a novel relevance index to inform the reduction process. We demonstrate the utility of the proposed approach, achieving a significant reduction of lattice nodes, even when the ontology only provides partial coverage of the domain of interest.

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Applying the JBOS reduction method for relevant knowledge extraction

Cited by 28 publications

References 27 publications

Fuzzy concept lattice reduction using Shannon entropy and Huffman coding

Fuzzy concept lattice reduction using Shannon entropy and Huffman coding

Cognitive concept learning from incomplete information

Ontology-Informed Lattice Reduction Using the Discrimination Power Index

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