Transforming the CIDOC-CRM Model Into a Megalithic Monument Property Graph
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| Titel: | Transforming the CIDOC-CRM Model Into a Megalithic Monument Property Graph |
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| Autoren: | Ariele Câmara, Ana de Almeida, João Oliveira |
| Weitere Verfasser: | Waagen, J., Verhagen, P., Hacigüzeller, P., Visser, R., Taelman, D., and Brandsen, A. |
| Quelle: | Journal of Computer Applications in Archaeology, Vol 7, Iss 1, Pp 213–224-213–224 (2024) |
| Verlagsinformationen: | Ubiquity Press, Ltd., 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | Knowledge graph, labeled property graph, dolmens, Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação, Labelled property graph, QA75.5-76.95, Neo4j, Neo4J, neo4j, Dolmen, Archaeology, knowledge graph, dolmen, CIDOC-CRM, Electronic computers. Computer science, Labeled property graph, Dolmens, cidoc-crm, Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática, Domínio/Área Científica::Humanidades::História e Arqueologia, labelled property graph, CC1-960 |
| Beschreibung: | This paper presents a method to store information about megalithic monuments' 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. Modeling a labeled 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 either between humans, between humans and machines and machine-to-machine. |
| Publikationsart: | Article Conference object |
| Dateibeschreibung: | application/pdf |
| Sprache: | English |
| ISSN: | 2514-8362 |
| DOI: | 10.5334/jcaa.151 |
| DOI: | 10.5281/zenodo.7991473 |
| DOI: | 10.5281/zenodo.10298687 |
| DOI: | 10.5281/zenodo.7981230 |
| DOI: | 10.5281/zenodo.7981231 |
| Zugangs-URL: | https://doaj.org/article/204fd13cfff9432d929503f565b829ec http://hdl.handle.net/10071/30421 http://hdl.handle.net/10071/31457 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....202472b3bc9ee1e21366da6324023edb |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | This paper presents a method to store information about megalithic monuments' 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. Modeling a labeled 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 either between humans, between humans and machines and machine-to-machine. |
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| ISSN: | 25148362 |
| DOI: | 10.5334/jcaa.151 |