Interesting Concept Mining With Concept Lattice Convolutional Networks
The extraction of meaningful conceptual structures is often a critical task in many scientific and engineering disciplines, as it enables a comprehensive analysis of complex data in terms of both context and content. In this paper, we introduce the Concept Lattice Convolutional Network (<inline-f...
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11027055/ |
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| author | Mohamed Hamza Ibrahim Rokia Missaoui Pedro Henrique B. Ruas |
| author_facet | Mohamed Hamza Ibrahim Rokia Missaoui Pedro Henrique B. Ruas |
| author_sort | Mohamed Hamza Ibrahim |
| collection | DOAJ |
| description | The extraction of meaningful conceptual structures is often a critical task in many scientific and engineering disciplines, as it enables a comprehensive analysis of complex data in terms of both context and content. In this paper, we introduce the Concept Lattice Convolutional Network (<inline-formula> <tex-math notation="LaTeX">$\mathcal {LCN}$ </tex-math></inline-formula>), an efficient semi-supervised learning approach to identify actionable concepts (i.e., interesting conceptual structures) based on a scalable convolutional neural network architecture that operates on concept lattices. The <inline-formula> <tex-math notation="LaTeX">$\mathcal {LCN}$ </tex-math></inline-formula> captures diverse levels of global context by employing a message-passing mechanism that incorporates local structural and conceptual information within a lattice. It also employs parameter-sharing convolutional operations as conceptual filters to efficiently discern relevant concepts amidst the irrelevant ones. Moreover, it applies consistent aggregations that maintain local consistency of labeling across concepts in the lattice. Experiments on several datasets show that <inline-formula> <tex-math notation="LaTeX">$\mathcal {LCN}$ </tex-math></inline-formula> can accurately identify actionable concepts and is at least three times faster than state-of-the-art exact interestingness indices. |
| format | Article |
| id | doaj-art-63f3b04bbd1d4eacbee46ca450340c3d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-63f3b04bbd1d4eacbee46ca450340c3d2025-08-20T03:25:38ZengIEEEIEEE Access2169-35362025-01-0113997899980110.1109/ACCESS.2025.357744511027055Interesting Concept Mining With Concept Lattice Convolutional NetworksMohamed Hamza Ibrahim0https://orcid.org/0000-0002-0604-2709Rokia Missaoui1https://orcid.org/0000-0001-7410-4177Pedro Henrique B. Ruas2https://orcid.org/0000-0001-6423-8681Computer Science and Engineering Department, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Computer Science and Engineering, University of Quebec in Outaouais, Gatineau, QC, CanadaEncora Digital Inc., Campinas, SÃo Paulo, BrazilThe extraction of meaningful conceptual structures is often a critical task in many scientific and engineering disciplines, as it enables a comprehensive analysis of complex data in terms of both context and content. In this paper, we introduce the Concept Lattice Convolutional Network (<inline-formula> <tex-math notation="LaTeX">$\mathcal {LCN}$ </tex-math></inline-formula>), an efficient semi-supervised learning approach to identify actionable concepts (i.e., interesting conceptual structures) based on a scalable convolutional neural network architecture that operates on concept lattices. The <inline-formula> <tex-math notation="LaTeX">$\mathcal {LCN}$ </tex-math></inline-formula> captures diverse levels of global context by employing a message-passing mechanism that incorporates local structural and conceptual information within a lattice. It also employs parameter-sharing convolutional operations as conceptual filters to efficiently discern relevant concepts amidst the irrelevant ones. Moreover, it applies consistent aggregations that maintain local consistency of labeling across concepts in the lattice. Experiments on several datasets show that <inline-formula> <tex-math notation="LaTeX">$\mathcal {LCN}$ </tex-math></inline-formula> can accurately identify actionable concepts and is at least three times faster than state-of-the-art exact interestingness indices.https://ieeexplore.ieee.org/document/11027055/Formal concept analysispattern mininggraph convolutional networkmessage-passinginterestingness measuresemi-supervised learning |
| spellingShingle | Mohamed Hamza Ibrahim Rokia Missaoui Pedro Henrique B. Ruas Interesting Concept Mining With Concept Lattice Convolutional Networks IEEE Access Formal concept analysis pattern mining graph convolutional network message-passing interestingness measure semi-supervised learning |
| title | Interesting Concept Mining With Concept Lattice Convolutional Networks |
| title_full | Interesting Concept Mining With Concept Lattice Convolutional Networks |
| title_fullStr | Interesting Concept Mining With Concept Lattice Convolutional Networks |
| title_full_unstemmed | Interesting Concept Mining With Concept Lattice Convolutional Networks |
| title_short | Interesting Concept Mining With Concept Lattice Convolutional Networks |
| title_sort | interesting concept mining with concept lattice convolutional networks |
| topic | Formal concept analysis pattern mining graph convolutional network message-passing interestingness measure semi-supervised learning |
| url | https://ieeexplore.ieee.org/document/11027055/ |
| work_keys_str_mv | AT mohamedhamzaibrahim interestingconceptminingwithconceptlatticeconvolutionalnetworks AT rokiamissaoui interestingconceptminingwithconceptlatticeconvolutionalnetworks AT pedrohenriquebruas interestingconceptminingwithconceptlatticeconvolutionalnetworks |