Formal Concept Analysis: From Knowledge Discovery to Knowledge Processing

Ferré, Sébastien; Huchard, Marianne; Kaytoue, Mehdi; Kuznetsov, Sergei O.; Napoli, Amedeo

doi:10.1007/978-3-030-06167-8_13

Cited by 20 publications

(10 citation statements)

References 110 publications

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“…Knowledge is generated when solutions to problems are developed and experience is gained. Also, knowledge creation can increase integrity and knowledge quality (Ferré et al, 2020). Therefore, students have an immense need for knowledge and not only students but also every other person searching for new information.…”

Section: Compatibilitymentioning

confidence: 99%

Knowledge and innovative factors: how cloud computing improves students’ academic performance

Raza

Khan

2021

ITSE

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Purpose Collaboration, communication, critical thinking and creativity are the most essential Cs of education. However, at present, these Cs are interlinked with technology to make it more effective and reliable. Educational technology infuses higher education, many people use it on a daily basis. Students are eager to adopt such technologies that help them in academia. Hence, this study aims to investigate how cloud computing adoption influences the academic performance of students by incorporating innovative, knowledge, economic and technological factors in the model. Design/methodology/approach The data are collected by using the survey method and the five-point Likert scale is used for this purpose. The statistical techniques applied to the data set were confirmatory factor analysis and partial least square structural equation modeling. Findings All dimensions have been observed to have a positive association with perceived ease of use and perceived usefulness. On the other hand, the innovative factors which include relative advantage and complexity have a negative impact on perceived ease of use and perceived usefulness except for compatibility. Moreover, economic factors, all have a negative relationship. Finally, research shows that perceived ease of use and perceived usefulness have a direct and significant relationship with cloud computing adoption among students, which ultimately predicts their academic performance. Originality/value Present research makes the following vital contributions; first, focus on the role of innovative factors, economical, technological and knowledge factors together that were previously largely ignored. Second, it extends the model of technology acceptance model for analyzing the cloud computing adoption pattern among university students. Finally, this study uses PLS-SEM for analyzing the relationship.

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Section: Compatibilitymentioning

confidence: 99%

Knowledge and innovative factors: how cloud computing improves students’ academic performance

Raza

Khan

2021

ITSE

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“…In [8], The FCA's potential applications include linguistic structure analysis, lexical semantics, and lexical tuning, to name a few. Recently, [3] proposed a novel approach to automatically acquiring concept hierarchies from domain-specific texts focused on FCA. The method was also compared to a hierarchical agglomerative clustering algorithm and Bi-Section-K-Means, and it was discovered that it worked better on two datasets.…”

Section: Related Literal Workmentioning

confidence: 99%

“…However, the end-result concept lattice may vary greatly from the original and, as a result, may be Fig. 1 NLP components (redrawn from [3]) of low quality. Table 2 summarises the key simplifier techniques for idea lattice reduction.…”

Section: Related Literal Workmentioning

confidence: 99%

“…This approach has the downside Fig. 2 A framework for automatic construction of concept hierarchies (adapted from [3]) of causing essential formal structures to be overlooked. Furthermore, some strategies compile a list of all formal concepts and use a collection of parameters to pick the ones that are applicable [39][40][41].…”

Section: Related Literal Workmentioning

confidence: 99%

“…Ontology learning is a subtask of knowledge extraction whose aim is to create ontologies by semi-automatically or automatically extracting related concepts and relationships from a corpus or other data sets. The current approach for automatic attainment of concepts and concept hierarchies form corpus is introduced by [3]. This approach is based on FCA to construct a concept lattice that can be converted to a particular type of partial order that constitutes a hierarchy of concepts.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Hassan

Rashid

Mirjalili

2021

Complex Intell. Syst.

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It 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.

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Application of LP Structures Theory to Intelligent Attribute Merger Refactoring

Makhortov

Nogikh

2020

Artificial Intelligence

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Formal Concept Analysis: From Knowledge Discovery to Knowledge Processing

Cited by 20 publications

References 110 publications

Knowledge and innovative factors: how cloud computing improves students’ academic performance

Knowledge and innovative factors: how cloud computing improves students’ academic performance

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

Application of LP Structures Theory to Intelligent Attribute Merger Refactoring

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