Please use this identifier to cite or link to this item: http://hdl.handle.net/10071/30421
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dc.contributor.authorCâmara, A.-
dc.contributor.authorAlmeida, A. de.-
dc.contributor.authorOliveira, J.-
dc.contributor.editorWaagen, J., Verhagen, P., Hacigüzeller, P., Visser, R., Taelman, D., and Brandsen, A.-
dc.date.accessioned2024-01-17T09:49:53Z-
dc.date.available2024-01-17T09:49:53Z-
dc.date.issued2024-
dc.identifier.citationCâmara, A., Almeida, A. de., & Oliveira, J. (2024). Transforming the CIDOC-CRM model into a megalithic monument property graph. In J. Waagen, P. Verhagen, P. Hacigüzeller, R. Visser, D. Taelman, & A. Brandsen (Orgs.), CAA2023 Conference Proceedings. https://doi.org/10.5281/zenodo.7981230-
dc.identifier.urihttp://hdl.handle.net/10071/30421-
dc.description.abstractThis 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. 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 between humans, between humans and machines and machine to machine.eng
dc.language.isoeng-
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04466%2F2020/PT-
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04466%2F2020/PT-
dc.relationinfo:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F151495%2F2021/PT-
dc.relation.ispartofCAA2023 Conference Proceedings-
dc.rightsopenAccess-
dc.subjectKnowledge grapheng
dc.subjectDolmeneng
dc.subjectCIDOC-CRMeng
dc.subjectLabeled property grapheng
dc.subjectNeo4Jeng
dc.titleTransforming the CIDOC-CRM model into a megalithic monument property grapheng
dc.typeconferenceObject-
dc.event.titleCAA 2023 International Conference on Archaeological Computing and Archaeological Data Analysis-
dc.event.typeConferênciapt
dc.event.locationAmsterdameng
dc.event.date2023-
dc.peerreviewedyes-
dc.date.updated2024-01-17T09:48:38Z-
dc.description.versioninfo:eu-repo/semantics/publishedVersion-
dc.identifier.doi10.5281/zenodo.7981230-
iscte.subject.odsEducação de qualidadepor
iscte.subject.odsIndústria, inovação e infraestruturaspor
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-100094-
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