Ontology-Based Semantic Retrieval for Museum News Systems

Authors

  • Supavit Phuvarit Khon Kaen University, Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen, Thailand Author https://orcid.org/0009-0000-4934-921X
  • Pongsathon Pookduang Rajamangala University of Technology Isan, Department of Information System, Faculty of Business Administration and Information Technology, Khon Kaen, Thailand Author https://orcid.org/0009-0007-1359-1195
  • Rapeepat Klangbunrueang Khon Kaen University, Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen, Thailand Author https://orcid.org/0009-0006-0869-6614
  • Sumana Chiangnangam Khon Kaen University, Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen, Thailand Author https://orcid.org/0009-0003-3516-681X
  • Wirapong Chansanam Khon Kaen University, Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen, Thailand Author https://orcid.org/0000-0001-5546-8485
  • Kulthida Tuamsuk Khon Kaen University, Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen, Thailand Author https://orcid.org/0000-0003-0852-8945
  • Tassanee Lunrasri Rajamangala University of Technology Isan, Department of Information System, Faculty of Business Administration and Information Technology, Khon Kaen, Thailand Author https://orcid.org/0009-0006-7211-7226

DOI:

https://doi.org/10.56294/dm20251147

Keywords:

Semantic Retrieval, Museum Ontology, SPARQL, Cultural Heritage, Ontology-Based Data Access

Abstract


Introduction: Museums face challenges in managing and retrieving timely news content due to fragmented information systems. This study investigates how semantic web technologies can enhance contextual accuracy and accessibility in museum information retrieval.
Methods: We created a domain-specific ontology integrated with relational databases via Ontology-Based Data Access (OBDA). A semantic search system accepting natural language queries was implemented and evaluated by experts using standard information retrieval metrics.
Results: The system achieved strong performance with precision of 0,85, recall of 0,96, and F1-score of 0,88, demonstrating effective semantic retrieval of museum news.
Conclusions: The findings demonstrate that semantic web technologies improve the accessibility and contextual relevance of museum news, contributing to digital heritage information management.

 

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Published

2025-08-09

Issue

Vol. 4 (2025): Data and Metadata

Section

Original

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Copyright (c) 2025 Supavit Phuvarit, Pongsathon Pookduang, Rapeepat Klangbunrueang, Sumana Chiangnangam, Wirapong Chansanam, Kulthida Tuamsuk, Tassanee Lunrasri (Author)

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How to Cite

1.
Phuvarit S, Pookduang P, Klangbunrueang R, Chiangnangam S, Chansanam W, Tuamsuk K, et al. Ontology-Based Semantic Retrieval for Museum News Systems. Data and Metadata [Internet]. 2025 Aug. 9 [cited 2025 Aug. 24];4:1147. Available from: https://dm.ageditor.ar/index.php/dm/article/view/1147