Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/31457
Autoria: Câmara, A.
Almeida, A. de.
Oliveira, J.
Data: 2024
Título próprio: Transforming the CIDOC-CRM model into a megalithic monument property graph
Título da revista: Journal of Computer Applications in Archaeology
Volume: 7
Número: 1
Paginação: 213 - 224
Referência bibliográfica: Câmara, A., Almeida, A. de., & Oliveira, J. (2024). Transforming the CIDOC-CRM model into a megalithic monument property graph. Journal of Computer Applications in Archaeology, 7(1), 213-224. https://doi.org/10.5334/jcaa.151
ISSN: 2514-8362
DOI (Digital Object Identifier): 10.5334/jcaa.151
Palavras-chave: Knowledge graph
Dolmens
CIDOC-CRM
Labelled property graph
Neo4j
Resumo: This paper presents a method to store information about megalithic monument-building components as graph nodes in a knowledge graph (KG). As a case study, we analyse the dolmens from the region of Pavia (Portugal). To build the KG, information has been extracted from unstructured data to populate a schema model based on the International Committee for Documentation – Conceptual Reference Model (CIDOC-CRM). In order to prepare the archaeological monument’s information for bulk loading, it was transformed into semi-structured data. While the semi-structured file was used to populate the classes with their respective properties and instances, the KG labels and types were defined using some of the entities and relations defined by the CIDOC-CRM. The knowledge-driven model was built to represent dolmens in a formal and structured manner using Neo4j, a property-graph database. Modelling a labelled property graph based on predefined labels as a KG enables to transform textual semantic data into instances and properties. Thus, we show that it is possible to represent at a granular level all the information about the structural components of monuments since heterogeneities, granularities, and large amounts of data can be handled by a KG. Therefore, a KG implemented using a native graph database can improve data storage and processing, making it interoperable between humans, humans and machines and machine to machine.
Arbitragem científica: yes
Acesso: Acesso Aberto
Aparece nas coleções:ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

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