Semantic interoperability of large systems through a formal method: Relational Concept Analysis

Wajnberg, Mickaël; Lezoche, Mario; Blondin-Massé, Alexandre; Valchev, Petko; Panetto, Hervé; Tyvaert, Louise

doi:10.1016/j.ifacol.2018.08.330

Cited by 5 publications

(2 citation statements)

References 6 publications

(8 reference statements)

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“…RCA was used in industrial information systems to make tools inter-operate, e.g. in the domain of brain activity measurement [35]. It was also applied to identify anomalies in aluminum die casting process description [34].…”

Section: Background and Motivationmentioning

confidence: 99%

Relational Concept Analysis in Practice: Capitalizing on Data Modeling Using Design Patterns

Braud

Dolques

Huchard

et al. 2023

Lecture Notes in Computer Science

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Many applications of Formal Concept Analysis (FCA) and its diverse extensions have been carried out in recent years. Among these extensions, Relational Concept Analysis (RCA) is one approach for addressing knowledge discovery in multi-relational datasets. Applying RCA requires stating a question of interest and encoding the dataset into the input RCA data model, i.e. an Entity-Relationship model with only Boolean attributes in the entity description and unidirectional binary relationships. From the various concrete RCA applications, recurring encoding patterns can be observed, that we aim to capitalize taking software engineering design patterns as a source of inspiration. This capitalization work intends to rationalize and facilitate encoding in future RCA applications. In this paper, we describe an approach for defining such design patterns, and we present two design patterns: "Separate/Gather Views" and "Level Relations".

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Section: Background and Motivationmentioning

confidence: 99%

Relational Concept Analysis in Practice: Capitalizing on Data Modeling Using Design Patterns

Braud

Dolques

Huchard

et al. 2023

Lecture Notes in Computer Science

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show abstract

“…In computer science, some studies were successful to apply formal concept analysis for solving some problems in many sub-domains, e.g ., datamining ( Aragón, Medina & Ramírez-Poussa, 2022 ; Hao et al, 2023 ), machine learning ( Janostik, Konecny & Krajča, 2022 ), data science ( Bazin et al, 2022 ), intelligent system ( Shao et al, 2023 ), information retrieval ( Ojeda-Hernández, López-Rodríguez & Mora, 2023 ; Khattak et al, 2021 ), natural language processing ( Marín et al, 2021 ; Jain, Seeja & Jindal, 2020 ), decision support system ( Wei et al, 2020 ), recommendation system ( Liu et al, 2022 ), semantic web ( Jindal, Seeja & Jain, 2020 ), cloud computing ( Khemili, Hajlaoui & Omri, 2022 ), data structure ( Ferré & Cellier, 2020 ), mobile application ( Kwon et al, 2021 ), software engineering ( Carbonnel et al, 2020 ), and robotic ( Zhang et al, 2023 ). In addition, some successful studies to apply formal concept analysis were in other domains, e.g ., engineering ( Rocco, Hernandez-Perdomo & Mun, 2020 ), mathematics ( Jäkel & Schmidt, 2022 ; Rocco, Hernandez-Perdomo & Mun, 2020 ), biology ( Gély et al, 2022 ), psychology ( Belohlavek & Mikula, 2022 ), medicine ( Md Saleh, Ab Ghani & Jilani, 2022 ), business ( Wajnberg et al, 2018 ; Ravi, Ravi & Prasad, 2017 ; Acharjya & Das, 2017 ), and social science ( Lang & Yao, 2023 ; Hao et al, 2021 ; Gao et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Non-redundant implicational base of formal context with constraints using SAT

Hidayat,

Ahmad,

Ngo

2024

PeerJ Computer Science

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An implicational base is knowledge extracted from a formal context. The implicational base of a formal context consists of attribute implications which are sound, complete, and non-redundant regarding to the formal context. Non-redundant means that each attribute implication in the implication base cannot be inferred from the others. However, sometimes some attribute implications in the implication base can be inferred from the others together with a prior knowledge. Regarding knowledge discovery, such attribute implications should be not considered as new knowledge and ignored from the implicational base. In other words, such attribute implications are redundant based on prior knowledge. One sort of prior knowledge is a set of constraints that restricts some attributes in data. In formal context, constraints restrict some attributes of objects in the formal context. This article proposes a method to generate non-redundant implication base of a formal context with some constraints which restricting the formal context. In this case, non-redundant implicational base means that the implicational base does not contain all attribute implications which can be inferred from the others together with information of the constraints. This article also proposes a formulation to check the redundant attribute implications and encoding the problem into satisfiability (SAT) problem such that the problem can be solved by SAT Solver, a software which can solve a SAT problem. After implementation, an experiment shows that the proposed method is able to check the redundant attribute implication and generates a non-redundant implicational base of formal context with constraints.

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Methods for concept analysis and multi-relational data mining: a systematic literature review

Leutwyler,

Lezoche,

Franciosi

et al. 2024

Knowl Inf Syst

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Semantic interoperability of large systems through a formal method: Relational Concept Analysis

Cited by 5 publications

References 6 publications

Relational Concept Analysis in Practice: Capitalizing on Data Modeling Using Design Patterns

Relational Concept Analysis in Practice: Capitalizing on Data Modeling Using Design Patterns

Non-redundant implicational base of formal context with constraints using SAT

Methods for concept analysis and multi-relational data mining: a systematic literature review

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