A New Automatic Ontology Construction Method Based on Machine Learning Techniques

Drissi, Amani; Khemiri, Ahmed; Sassi, Salma; Chbeir, Richard

doi:10.1145/3444757.3485111

Cited by 4 publications

(5 citation statements)

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“…From the review, most of the mentioned papers have identified their automation in the ontology specification phase which is the first phase of the standard methodology as shown in Figure 1. For example [17], [22], [30] and [31] has done their automatic construction in ontology specification phase. Meanwhile, [19] has done automation in the ontology conceptualization phase, the second phase of the methodology, and [12] in the implementation phase which is the third phase of the methodology.…”

Section: Answer To Research Question Rq2 What Is the Methodology Used...mentioning

confidence: 99%

“…As can be seen from Table 6, most of the papers have done automatic construction in the ontology specification phase by doing term extraction activity. Sharef et al [11] has used the engine based on POS tagging coupled with patternbased extraction techniques to automatically extracts the candidate terms from the competency questions, in [17] has used TF-IDF measure to extract relevant concepts from the corpus, in [22] used relevance score based on DP and DC to extract useful terms, and [23] performs the activity of constructor and converter for pre-processing and use TF-IDF for term weighing. Besides, [30] has applied NLP to extract important information from academic papers by using Python 2.7 and NLTK, meanwhile [31] has used a term scoring algorithm for term and phrases extraction from textual corpus to generate the domain terminology.…”

Section: Answer To Research Question Rq2 What Is the Methodology Used...mentioning

confidence: 99%

“…Adopt a data-driven strategy to find the preposition that can most likely be used to specify the "object property (X, Y)" expression. Paper 9 [17] Phase: Ontology specification…”

Section: Activity: General Object Property Relations Specification −mentioning

confidence: 99%

“…Ontology learning (OL) is a semi-automated process for creating, maintaining, and transferring various types of information into an ontology with minimal human intervention to ensure better knowledge representation and sharing [17]. OL is the most important step toward lowering the cost of ontology building, which refers to a collection of semi-automated frameworks for creating and maintaining ontologies [18].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we have identified several methods and methodologies proposed for developing ontologies automatically. For example, to construct semi-automatic ontology, Semi-automatic Sentiment Domain Ontology Building Using Synsets (SASOBUS) methodology was proposed by [22] where term selection phase as its automate part; Sabença framework in the form of modular methodology [23] where constructor, converter, weigher, extractor and exporter modules as automating part; Norms2Onto, a semiautomatic ontology construction method based on machine learning algorithms [17]; Learn2Construct, LDAbased construction learning method [18] to construct fully automatic ontology; WEB2ONTO, an approach for constructing ontology automatically from web pages [24]; ontology learning method and many others. This paper will assist fellow researchers and practitioners in better understanding, and thus implementing, the methods of automatic ontology construction in various fields.…”

Section: Introductionmentioning

confidence: 99%

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A systematic literature review of automatic ontology construction

Zulkipli

Maskat

Teo

2022

IJEECS

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In recent years, ontologies have received a lot of attention as a knowledge representation technique. Constructing an ontology can be a difficult task for several reasons: it necessitates time-consuming expert work, the classification task is not as simple as it appears, and the incredible speed with which knowledge evolves in the real world forces ontology engineers to constantly update and enrich the generated ontologies with new concepts, terms, and lexicon. However, there are other ways to automate ontology construction, such as semi-automatically, where human intervention is required in one or more ontology design tasks, and fully automatically, where the entire ontology construction is delegated to a software system. In this paper, we will conduct a systematic review of literature that will focus on a comparative analysis of different techniques relating to both semi-automatic and fully automatic ontology construction using various techniques and automated algorithms applied. In these fields, the goal is to identify the domain areas, current trends, data architectures, and ongoing challenges. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. The fields that have been investigated in this survey include online retail, biomedical, public security, information security (IS), Quran, Arabic, Dubai government services, Alzheimer’s disease, agriculture, Chinese tax, job portal, sentiment, and ontology learning. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.

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Section: Answer To Research Question Rq2 What Is the Methodology Used...mentioning

confidence: 99%

Section: Answer To Research Question Rq2 What Is the Methodology Used...mentioning

confidence: 99%

“…Adopt a data-driven strategy to find the preposition that can most likely be used to specify the "object property (X, Y)" expression. Paper 9 [17] Phase: Ontology specification…”

Section: Activity: General Object Property Relations Specification −mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A systematic literature review of automatic ontology construction

Zulkipli

Maskat

Teo

2022

IJEECS

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Introduction to Ontologies for Defense Business Analytics

Taylor

Izumigawa

Sato

2023

Lecture Notes in Computer Science

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Metodologias sobre aprendizagem de ontologias: uma revisão sistemática de literatura

Lopes,

Lima

2024

Revista Científica Multidisciplinar Núcleo do Conhecimento

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Contextualização: Ontologias vêm ganhando popularidade e reconhecimento em diferentes contextos. Entretanto, com o aumento exponencial de dados não estruturados na Web, o processo de construção de ontologias tornou-se complexo, demorado e dispendioso. Surgem, então, as abordagens de geração automática ou semiautomática de ontologias. Dado o contexto atual de transformação digital pelo qual passam diversos países, incluindo o Brasil, entende-se que as pesquisas sobre a automatização da geração de ontologias podem contribuir para a exploração e melhor aplicação desse tipo de Sistema de Organização do Conhecimento (SOC) no âmbito do governo eletrônico. Objetivo: Este estudo apresenta uma Revisão Sistemática da Literatura (RSL) sobre a geração automática e semiautomática de ontologias, com o objetivo de evidenciar o estágio atual da pesquisa nessa área. Métodos: Adotou-se o método proposto por Kitchenham (2004), que descreve procedimentos para realizar revisões sistemáticas de literatura, considerando a formulação de perguntas de pesquisa, o desenvolvimento de um protocolo, a identificação e seleção de estudos, avaliação de qualidade, extração e síntese de dados e relato dos resultados. Resultados: A revisão revelou uma diversidade significativa de abordagens e metodologias sendo exploradas para a geração automática e semiautomática de ontologias. No entanto muitas metodologias ainda enfrentam desafios, principalmente no que diz respeito ao detalhamento de fases e etapas que sejam facilmente replicáveis. Conclusões: Futuras pesquisas devem focar na criação de metodologias mais detalhadas e sistemáticas para aumentar a replicabilidade e a aplicação prática. As descobertas deste estudo podem inspirar investigações adicionais e estimular a implementação das ontologias no âmbito no governo eletrônico, com vistas a aprimorar a eficiência, a transparência e a acessibilidade dos serviços públicos digitais.

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A New Automatic Ontology Construction Method Based on Machine Learning Techniques

Cited by 4 publications

References 5 publications

A systematic literature review of automatic ontology construction

A systematic literature review of automatic ontology construction

Introduction to Ontologies for Defense Business Analytics

Metodologias sobre aprendizagem de ontologias: uma revisão sistemática de literatura

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