Automatic Creation of Ontology Using a Lexical Database: An Application for the Energy Sector

Moreira, Alexandra; Filho, Jugurta Lisboa; Oliveira, Alcione de Paiva

doi:10.1007/978-3-319-41754-7_42

Cited by 2 publications

(4 citation statements)

References 3 publications

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“…(2) This method starts with empty objects and attributes every time, which requires a lot of work and is only suitable for the construction of a small domain ontology. After introducing formal concept analysis into domain ontology construction, researchers have solved some key problems in domain ontology construction to a certain extent [25][26][27][28][29][30], including the semi-automatic construction and the formalization of ontology; the relation mining between implicit concepts; the degree of subjective influence and ontology sharing and reuse; rich ontology semantics; and the visualization of an ontology model based on a concept lattice.…”

Section: Classical Research Limitationsmentioning

confidence: 99%

Research on Knowledge Graphs with Concept Lattice Constraints

et al. 2021

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The application of knowledge graphs has been restricted in some domains, especially the industrial and academic domains. One of the reasons is that they require a high reliability of knowledge, which cannot be satisfied by the existing knowledge graph research. By comparison, traditional knowledge engineering has a high correctness, but low efficiency is an inevitable drawback. Therefore, it is meaningful to organically connect traditional knowledge engineering and knowledge graphs. Therefore, we propose a theory from Attribute Implications to Knowledge Graphs, named AIs-KG, which can construct knowledge graphs based on implications. The theory connects formal concept analysis and knowledge graphs. We firstly analyze the mutual transformation based on the ideas of symmetry with a strict proof among the attribute implication, the formal context and the concept lattice, which forms the closed cycle between the three. Particularly, we propose an Augment algorithm (IFC-A) to generate the Implication Formal Context through the attribute implication, which can make knowledge more complete. Furthermore, we regard ontology as a bridge to realize the transformation from the concept lattice to the knowledge graph through some mapping methods. We conduct our experiments on the attribute implication from the rule base of an animal recognition expert system to prove the feasibility of our algorithms.

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Section: Classical Research Limitationsmentioning

confidence: 99%

Research on Knowledge Graphs with Concept Lattice Constraints

et al. 2021

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“…This section describes how the AutOnGen module works. The module was partially described in [Moreira et al 2016], but was not analyzed whether the technique used was statistically relevant. In the results section of this article this analysis is presented.…”

Section: The Ontology Building Modulementioning

confidence: 99%

“…The term anchor must be carefully chosen, and some tests should be performed to determine the best choice. As stated by [Moreira et al 2016], the domain anchor may ensure, for instance, that the term "acordo" be translated as "agreement" and not "wake up" by simply adding the term "negócios" (business) as domain anchor.…”

Section: The Ontology Building Modulementioning

confidence: 99%

“…Thus, for the term "business" and the hypernym candidate term "group"is formed the compound term "group business". According to [Moreira et al 2016], the underlying hypothesis of this technique is that the construction <noun noun>, where the second term modifies or qualifies the first, it is a common linguistic construction in the English language. This type of compound was called subsumptive compound by Marchand [Marchand 1969] apud [Lieber and Stekauer 2009].…”

Section: The Ontology Building Modulementioning

confidence: 99%

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Evaluating Ontology Development from the Extraction of Noun Phrases

Oliveira¹,

Moreira²,

Filho³

2019

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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Existem vários métodos para construir uma ontologia. Entre o métodos automáticos, uma abordagem é a extração de termos de documentos de domínio e sua extração subsequente. Nesse caso, a primeira etapa do processo é a extração de sintagmas nominais que são possíveis candidatos a serem componentes da terminologia da área de interesse. Este artigo descreve uma ferramenta automática para o idioma português do Brasil que extrai sintagmas nominais que podem ser adotadas como termos para um determinado domínio. Além disso, o sistema associa os termos extraídos a uma ontologia de nível superior, o que resulta em uma ontologia inicial que pode ser refinada posteriormente. Para acoplar à ontologia, foi utilizado um termo âncora, sendo demonstrado estatisticamente que o uso do termo âncora leva a uma melhoria no desempenho do sistema. A ferramenta descrita neste artigo foi usada para selecionar termos a serem usados em uma ontologia para o domínio do setor eleétrico. Além disso, foi avaliada a precisão na criação da ontologia. A técnica conseguiu gerar a hierarquia correta para 70% dos termos

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Automatic Creation of Ontology Using a Lexical Database: An Application for the Energy Sector

Cited by 2 publications

References 3 publications

Research on Knowledge Graphs with Concept Lattice Constraints

Research on Knowledge Graphs with Concept Lattice Constraints

Evaluating Ontology Development from the Extraction of Noun Phrases

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