TY - JOUR

T1 - A method of semi-automated ontology population from multiple semi-structured data sources

AU - Leshcheva, Irina

AU - Begler, Alena

N1 - Leshcheva I. A method of semi-automated ontology population from multiple semi-structured data sources / I. Leshcheva, A. Begler // Journal of Information Science. - 2020. - URL: https://journals.sagepub.com/doi/10.1177/0165551520950243 Publisher Copyright: © The Author(s) 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2022

Y1 - 2022

N2 - Organisations use data in different formats: Word documents, Excel spreadsheets, databases, HTML pages and so on. It is not easy to make decisions with such data due to the lack of integration between the different sources and built-in decision-making rules. Decisions can be reached with knowledge bases, which, unlike databases, make it possible to store not only objects, facts and attributes but also more sophisticated patterns such as rules and axioms. The article proposes an ontology-based method for knowledge base creation that allows for the simultaneous integration of semi-structured data sources and extendibility while remaining context independent. At the initial steps of the method, data specification should be performed with the Data Sources Ontology developed by the authors. This ontology provides data structure description that forms supportive knowledge graph. The graph’s schema should be mapped with the domain ontology to be populated. Finally, the data are inserted into the domain ontology according to the mapping rules. Manual input is needed during data specification and data-to-ontology schema mapping.

AB - Organisations use data in different formats: Word documents, Excel spreadsheets, databases, HTML pages and so on. It is not easy to make decisions with such data due to the lack of integration between the different sources and built-in decision-making rules. Decisions can be reached with knowledge bases, which, unlike databases, make it possible to store not only objects, facts and attributes but also more sophisticated patterns such as rules and axioms. The article proposes an ontology-based method for knowledge base creation that allows for the simultaneous integration of semi-structured data sources and extendibility while remaining context independent. At the initial steps of the method, data specification should be performed with the Data Sources Ontology developed by the authors. This ontology provides data structure description that forms supportive knowledge graph. The graph’s schema should be mapped with the domain ontology to be populated. Finally, the data are inserted into the domain ontology according to the mapping rules. Manual input is needed during data specification and data-to-ontology schema mapping.

KW - Data Source Ontology

KW - ontology population

KW - ontology-based data integration

KW - semi-structured data

KW - SCOPUS

UR - http://www.scopus.com/inward/record.url?scp=85089701537&partnerID=8YFLogxK

U2 - 10.1177/0165551520950243

DO - 10.1177/0165551520950243

M3 - Article

AN - SCOPUS:85089701537

VL - 48

SP - 223

EP - 236

JO - Journal of Information Science

JF - Journal of Information Science

SN - 0165-5515

IS - 2

ER -