Ontology-Based Semantic Retrieval for Museum News Systems

Supavit Phuvarit, Pongsathon Pookduang, Rapeepat Klangbunrueang, Sumana Chiangnangam, Wirapong Chansanam, Kulthida Tuamsuk and Tassanee Lunrasri

Data and Metadata, 2025, vol. 4, 1147

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.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:1147:id:1056294dm20251147

DOI: 10.56294/dm20251147

Access Statistics for this article

More articles in Data and Metadata from AG Editor
Bibliographic data for series maintained by Javier Gonzalez-Argote ().