Domain Ontology Construction using Formal Concept and Relational Concept Analysis

2021 ◽  
Author(s):  
M.Farida Begam
Keyword(s):  
Concept Analysis ◽  
Domain Ontology ◽  
Formal Concept ◽  
Ontology Construction ◽  
Relational Concept

Tourism Domain Ontology Construction Method Based on Fuzzy Formal Concept Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 2809-2813
Author(s):  
Yan Liu ◽  
Sheng Quan Liu ◽  
Peng Li
Keyword(s):  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Domain Ontology ◽  
Formal Concept ◽  
Uncertain Information ◽  
Construction Process ◽  
Ontology Construction ◽  
Concept Hierarchy ◽  
Fuzzy Formal Concept ◽  
Fuzzy Formal Concept Analysis

In this paper, fuzzy formal concept analysis is introduced to the tourism domain ontology construction process, first fuzzy formal concept analysis of uncertain information in the domain of tourism,then through the conceptual clustering generated fuzzy concept hierarchy, lastly mapping to get fuzzy ontology prototype. A example shows that the method is feasible and effective.

scholarly journals Exploring Variability of Visual Accessibility Options in Operating Systems

2021 ◽  
Vol 13 (9) ◽  
pp. 230
Author(s):  
Austin Waffo Kouhoué ◽  
Yoann Bonavero ◽  
Thomas Bouétou Bouétou ◽  
Marianne Huchard
Keyword(s):  
Operating Systems ◽  
Formal Concept Analysis ◽  
Digital Technologies ◽  
Concept Analysis ◽  
Formal Concept ◽  
Significant Variability ◽  
Relational Concept ◽  
Variability Model ◽  
The Way

Digital technologies are an opportunity to overcome disabilities, provided that accessibility is ensured. In this paper, we focus on visual accessibility and the way it is supported in Operating Systems (OS). The significant variability in this support has practical consequences, e.g., the difficulty to recommend or select an OS, or migrate from one OS to another. This suggests building a variability model for OS that would classify them and would serve as a reference. We propose a methodology to build such a variability model with the help of the Formal Concept Analysis (FCA) framework. In addition, as visual accessibility can be divided into several concerns (e.g., zoom, or contrast), we leverage an extension of FCA, namely Relational Concept Analysis. We also build an ontology to dispose of a standardized description of visual accessibility options. We apply our proposal to the analysis of the variability of a few representative operating systems.

Ontology Construction Using Multiple Concept Lattices

2014 ◽  
Vol 926-930 ◽  
pp. 1975-1978
Author(s):  
Wen Chao Wang ◽  
Jiang Lu
Keyword(s):  
Formal Concept Analysis ◽  
Domain Ontology ◽  
Complex Domain ◽  
Formal Concept ◽  
Target Domain ◽  
Concept Lattices ◽  
Ontology Construction ◽  
Large Domain ◽  
Fuzzy Formal Concept ◽  
Fuzzy Formal Concept Analysis

The paper proposes an ontology construction approach that combines Fuzzy Formal Concept Analysis, Wikipedia and WordNet in a process that constructs multiple concept lattices for sub-domains. Those sub-domains are divided from the target domain. The multiple concept lattices approach can mine concepts and determine relations between concepts automatically, and construct domain ontology accordingly. This approach is suitable for the large domain or complex domain which contains obvious sub-domains.

Semantic Web Ontology Integration Based on Formal Concept Analysis

2013 ◽  
Vol 373-375 ◽  
pp. 1714-1718
Author(s):  
Hong Xia
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Formal Concept Analysis ◽  
Service Discovery ◽  
Concept Analysis ◽  
Domain Ontology ◽  
Ontology Engineering ◽  
Formal Concept ◽  
Modeling Concept

Matchmaking is the basis of doing service discovery and composition. Using ontology semantically express the service of capabilities, correctly match service. Domain ontology and Formal Concept Analysis aim at modeling concept. The role of FCA in ontology engineering is supporting reusing independently developed domain ontology. Evaluating concept similarity identifies the different concepts that are semantically close. In this paper, using concept and attribute of web services to construct the ontology. Also, an ontology based method for assessing similarity between FCA concepts is proposed.

A Concept Lattice Merger Approach for Ontology Construction

2011 ◽  
Vol 181-182 ◽  
pp. 667-672
Author(s):  
Bao Chuan Han ◽  
Ya Jun Du ◽  
Chang Wang ◽  
Jing Xu
Keyword(s):  
Semantic Representation ◽  
Concept Lattice ◽  
Domain Ontology ◽  
Semantic Relations ◽  
Formal Concept ◽  
Concept Lattices ◽  
Ontology Construction ◽  
Semantic Query ◽  
Hierarchical Relations ◽  
Ontology Tree

The method of merging concept lattice in domain ontology construction can describe the implicit concepts and relationships between concepts more appropriately for semantic representation and query match. In order to enrich semantic query, the paper intends to apply the theory of Formal Concept Analysis (FCA) to establish source concept lattices, through which the domain concepts are extracted from source concept lattices to generate the optimized concept lattice. Then, the ontology tree is generated by lattice mapping ontology algorithm (LMOA) combing some hierarchical relations in the optimized concept lattice. The experiment proves that the domain ontology can be achieved effectively by merging concept lattices and provide the semantic relations more precisely.

A Concept Lattice Merger Approach for Ontology Construction

2011 ◽  
Vol 181-182 ◽  
pp. 754-759
Author(s):  
Bao Chuan Han ◽  
Ya Jun Du ◽  
Chang Wang ◽  
Jing Xu
Keyword(s):  
Semantic Representation ◽  
Concept Lattice ◽  
Domain Ontology ◽  
Semantic Relations ◽  
Formal Concept ◽  
Concept Lattices ◽  
Ontology Construction ◽  
Semantic Query ◽  
Hierarchical Relations ◽  
Ontology Tree

The method of merging concept lattice in domain ontology construction can describe the implicit concepts and relationships between concepts more appropriately for semantic representation and query match. In order to enrich semantic query, the paper intends to apply the theory of Formal Concept Analysis (FCA) to establish source concept lattices, through which the domain concepts are extracted from source concept lattices to generate the optimized concept lattice. Then, the ontology tree is generated by lattice mapping ontology algorithm (LMOA) combing some hierarchical relations in the optimized concept lattice. The experiment proves that the domain ontology can be achieved effectively by merging concept lattices and provide the semantic relations more precisely.

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