Cidoc-CRM and Machine Learning
A Survey and Future Research
Bibliographic Data
| ID | 13125686 |
|---|---|
| Authors | Yannis Tzitzikas (0000-0001-8847-2130, University of Crete), Michalis Mountantonakis (0000-0002-1951-0241, University of Crete, corresponding author), Pavlos Fafalios (0000-0003-2788-526X, FORTH Institute of Computer Science), Yannis Marketakis (0000-0002-0417-2526, FORTH Institute of Computer Science) |
| Year | 2022 |
| Volume | 5 |
| Issue | 3 |
| Pages | 1612-1636 |
| Publication date | 2022-07-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Heritage (JOURNAL) |
| Journal identifiers | ISSN: 2571-9408 • E-ISSN: 2571-9408 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/heritage5030084 |
| OpenAlex | W4316926967 |
| Language | EN |
| Citations received | 2 |
| References cited | 60 |
The CIDOC Conceptual Reference Model (CIDOC-CRM) is an ISO Standard ontology for the cultural domain that is used for enabling semantic interoperability between museums, libraries, archives and other cultural institutions. For leveraging CIDOC-CRM, several processes and tasks have to be carried out. It is therefore important to investigate to what extent we can automate these processes in order to facilitate interoperability. For this reason, in this paper, we describe the related tasks, and we survey recent works that apply machine learning (ML) techniques for reducing the costs related to CIDOC-CRM-based compliance and interoperability. In particular, we (a) analyze the main processes and tasks, (b) identify tasks where the recent advances of ML (including Deep Learning) would be beneficial, (c) identify cases where ML has been applied (and the results are successful/promising) and (d) suggest tasks that can benefit from applying ML. Finally, since the approaches that leverage both CIDOC-CRM data and ML are few in number, (e) we introduce our vision for the given topic, and (f) we provide a list of open CIDOC-CRM datasets that can be potentially used for ML tasks
Information retrieval · Interoperability · Leverage (statistics · World Wide Web · Computer Science · Data Quality and Management · Digital and Traditional Archives Management · Semantic Web and Ontologies · Artificial Intelligence
SSD
Machine Learning for Cultural Heritage
You Only Look Once
Semantic Bridging of Cultural Heritage Disciplines and Tasks
LODsyndesis
Statistical processing of compositional data. The case of ceramic samples from the archaeological site of Xalasco, Tlaxcala, Mexico
An experimental design for the classification of archaeological ceramic data from Cyprus, and the tracing of inter-class relationships
Machine Learning Arrives in Archaeology
Bell Beaker and the evolution of resource management strategies in the southwest of the Iberian Peninsula
| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |