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Embedding Formal Contexts Using Unordered Composition

Esteban Marquer 1 Ajinkya Kulkarni 2 Miguel Couceiro 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Despite their simplicity, formal contexts possess a complex latent structure that can be exploited by formal context analysis (FCA). In this paper, we address the problem of representing formal contexts using neural embeddings in order to facilitate knowledge discovery tasks. We propose Bag of Attributes (BoA), a dataset agnostic approach to capture the latent structure of formal contexts into embeddings. Our approach exploits the relation between objects and attributes to generate representations in the same embedding space. Our preliminary experiments on attribute clustering on the SPECT heart dataset, and on co-authorship prediction on the ICFCA dataset, show the feasibility of BoA with promising results.
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https://hal.archives-ouvertes.fr/hal-02912874
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Submitted on : Thursday, August 6, 2020 - 11:54:41 PM
Last modification on : Monday, February 15, 2021 - 1:49:02 PM
Long-term archiving on: : Monday, November 30, 2020 - 4:07:37 PM

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  • HAL Id : hal-02912874, version 1

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Esteban Marquer, Ajinkya Kulkarni, Miguel Couceiro. Embedding Formal Contexts Using Unordered Composition. FCA4AI - 8th International Workshop "What can FCA do for Artificial Intelligence?" (colocated wit ECAI2020), Aug 2020, Santiago de Compostela, Spain. ⟨hal-02912874⟩

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