A strategy for archives metadata representation on CIDOC-CRM and knowledge discovery

Semantic web

article

Dora Melo¹  Irene Pimenta Rodrigues⁴  Davide Varagnolo⁴ 

[1] Coimbra Business School;NOVA Laboratory for Computer Science and Informatics, NOVA LINCS;Polytechnic of Coimbra, Coimbra Business School Research Center;Department of Informatics, University of Évora

关键词: Knowledge representation and reasoning;    natural language processing;    archives ontology;    semantic migration;    CIDOC-CRM;    linked data science;   

DOI  :  10.3233/SW-222798

来源: IOS Press

【 摘 要 】

This paper presents a strategy for the semantic migration of Portuguese National Archives records into CIDOC-CRM standard, an ontology developed for museums, within the context of the EPISA project. The approach to automatically populate the CIDOC-CRM is based on Mapping Description Rules to semantically translate the archives descriptive information into CIDOC-CRM representation. The compliance of the CIDOC-CRM model recommendations guarantees that the populated CIDOC-CRM ontology of archives descriptive information verifies interoperability, and could be linked and integrated with other populated CIDOC-CRM ontologies. In the information modelling, requirements on the mapping representation, due to the intent of interpreting natural language text to automatically extract information of metadata text fields and to interpret natural language queries, are taken into account. To automatically interpret the Mapping Description Rules, OWL API was used to obtain the set of assertions that represents the information in the target ontology and two datasets are available with some migration examples. The exploration of the knowledge representation is done through some Description Logic queries to highlight the advantages of having this new representation of the National Archives. The evaluation of the resulting representation can be done automatically proving its correctness for the metadata that has a direct representation in CIDOC-CRM.

【 授权许可】

Unknown