Application of Ontologies And Meta-Models for Dynamic Integration of Weakly Structured Data

Berko, Andrii; Pelekh, Irina; Chyrun, Lyubomyr; Bublyk, Myroslava; Bobyk, Іgor; Matseliukh, Yurii

doi:10.1109/dsmp47368.2020.9204321

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Dynamic integration [2] is a principle of weakly structured data integration from web-system. The weakly structured data are transformed in the integration process.…”

Section: Heterogeneous Data Source Integrationmentioning

confidence: 99%

“…Ontology is a knowledge base used to store con- In this research, data from heterogeneous sources are automatically merged to construct COVID-19 knowledge by using ontology engineering and text analytics. Web-based services, such as HyperText 1,2 The authors are with Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand 50200, E-mail: patipon_wi@cmu.ac.th and areerat.t@cmu.ac.th 2 The corresponding author: areerat.t@cmu.ac.th…”

Section: Introductionmentioning

confidence: 99%

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Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19

Wiangnak,

Trongratsameethong

2023

ECTI-CIT Transactions

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Gathering information from multiple data sources takes a long time to collect, analyze, and classify. Furthermore, if the data sources have different data structures, the merged data structure must support such heterogeneity. In addition, semantic of data must also be considered. This paper proposes automated knowledge integration from heterogeneous data sources using ontology engineering combined with text analytics. Text stemming is used to preprocess data. Part-of-speech (POS) tagging, Universal Dependencies (UD), and text similarity measurement called cosine similarity are used to analyze and integrate data. Our work focuses on five COVID- 19 knowledge scopes: COVID-19, coronaviruses, diseases, pandemics, and vaccines. For evaluation, six ontologies were constructed with six different cosine similarity values ranging from 0.5 to 1.0. Each constructed ontology has COVID-19 related and non-COVID-19 data in a ratio of 70 to 30. The six constructed ontologies were evaluated for consistency with the original data. Using cosine similarity with 0.6, precision, recall, and F1-score are 0.82, 0.71, and 0.76, respectively, and the constructed ontology is optimal, containing the highest amount of relevant COVID-19 information for this case study.

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“…Dynamic integration [2] is a principle of weakly structured data integration from web-system. The weakly structured data are transformed in the integration process.…”

Section: Heterogeneous Data Source Integrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19

Wiangnak,

Trongratsameethong

2023

ECTI-CIT Transactions

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“…On the other hand, applying formal models to partially describe the knowledge of the standards might be an alternative to tackle the aforementioned issues. In that sense, the ontologies, represented by means of Ontology Web Language (OWL), are an artificial intelligence technique that has been used to represent and analyze knowledge in different domains [4][5][6]. The ontologies allow to represent the knowledge of a domain and support the reasoning tasks on the concepts [7].…”

Section: Introductionmentioning

confidence: 99%

An ontology-based approach to support the knowledge management of software quality standards

Silega,

Castro Aguilar,

Martillo Alcívar

et al. 2023

Enfoque UTE

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Nowadays, the quality of software systems is crucial for companies to provide high-quality services and products. However, a wide number of software projects still fail. To increase the success probability of projects, it is suitable to adopting software quality standards to guide the process. However, standards are commonly described by means of natural language making difficult its analysis. For example, it is not easy to choose the most suitable standard according to the characteristics of a project. Furthermore, the usage of natural language hinders the automatic detection of inconsistencies and ambiguities. On the other hand, ontologies are an artificial intelligence technique that has been successfully used to represent and analyze knowledge in numerous domains because its capacity to enable the automatic validation and consistency checking of the represented information. This paper aims to present an ontology-based approach to describe and analyze software quality standards. Since this ontology can represent the knowledge of several standards, a reasoner may automatically validate the information and infer new knowledge. This ontology might support the reduction of conceptual ambiguity of standards descriptions and improve its understanding.

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The Lexical Innovations Identification in English-Language Eurointegration Discourse for the Goods Analysis by Comments in E-Commerce Resources

Lytvyn

Danylyk

Bublyk

et al. 2021

2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT)

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Application of Ontologies And Meta-Models for Dynamic Integration of Weakly Structured Data

Cited by 8 publications

References 28 publications

Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19

Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19

An ontology-based approach to support the knowledge management of software quality standards

The Lexical Innovations Identification in English-Language Eurointegration Discourse for the Goods Analysis by Comments in E-Commerce Resources

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