Workflow models for aggregating cultural heritage data on the web: A systematic literature review

Abstract

In recent years, different cultural institutions have made efforts to spread culture through the construction of a unique search interface that integrates their digital objects and facilitates data retrieval for lay users. However, integrating cultural data is not a trivial task; therefore, this work performs a systematic literature review on data aggregation workflows, in order to answer five questions: What are the projects? What are the planned steps? Which technologies are used? Are the steps performed manually, automatically, or semi-automatically? Which perform semantic search? The searches were carried out in three databases: Networked Digital Library of Theses and Dissertations, Scopus and Web of Science. In Q01, 12 projects were selected. In Q02, 9 stages were identified: Harvesting, Ingestion, Mapping, Indexing, Storing, Monitoring, Enriching, Displaying, and Publishing LOD. In Q03, 19 different technologies were found it. In Q04, we identified that most of the solutions are semiautomatic and, in Q05, that most of them perform a semantic search. The analysis of the workflows allowed us to identify that there is no consensus regarding the stages, their nomenclatures, and technologies, besides presenting superficial discussions. But it allowed to identify the main steps for the implementation of the aggregation of cultural data.

[Retracted] The Characteristics and Paths of the Dissemination of Intangible Cultural Heritage in the Form of Animation in the New Media Environment

“…At the same time, the relevant personnel also encountered some difficulties in the process of protection. The dissemination of intangible cultural heritage is a dynamic transmission with human beings as the carrier, which is transmitted by oral transmission [ 10 ]. This inheritance method is very dependent on the inheritors, and it is very easy to cause the decay or loss of cultural connotations in the process of dissemination.…”

Information organization and representation in digital cultural heritage in Brazil: Systematic mapping of information infrastructure in digital collections for data science applications

“…As observed in the diagnosis, as it is not a practice on part of Brazilian cultural institutions to adopt contemporary information systems such as digital repositories, which could work as a database and metadata management of the collections available on the network, the location function (“F”) of digital objects without a consistent and persistent identification (e.g., URI, IRI, among others), usually promoted by repository technology (Siqueira & Martins, 2021; Wilkinson et al, 2016), is compromised and dependent on exploration by human users or computational agents in static HTML pages with objects and their metadata dispersed and deprived of formal and intuitive semantics in terms of search, navigation, collection, extraction, mapping, enrichment, among other possible corresponding human or automated actions.…”

“…The ability to use good quality metadata from processes that reflect the use of controlled vocabularies, cataloging rules, and well‐defined metadata models expands the potential of DS applications. These applications may be less dedicated to the data preprocessing steps, generally aimed at improving the quality of the collected data (Siqueira & Martins, 2021), and more to the data analysis, algorithmic modeling, and pattern identification steps (Europeana Tech, 2021; IFLA, 2020).…”

“…The results obtained provide conceptual and analytical insight for future projects aimed at constructing a DS infrastructure for research in Brazilian digital cultural collections, similarly to projects already underway in other countries and political blocks, such as Europeana 2 in the European Union, the Digital Public Library of America (DPLA) 3 in the United States, Trove 4 in Australia, DigitalNZ 5 in New Zealand and Mexicana 6 in Mexico. Siqueira and Martins (2021) describe how several cultural heritage data aggregators built their aggregation models and details the technical steps of data treatment and analysis (e.g., collection, cleaning, normalization, reconciliation, among others) performed by each initiative. The paper ends by showing the technologies used (e.g., Elastick Stack, Apache Solr, Hadoop, among others) to implement the data science techniques used in each stage of the proposed information infrastructure.…”

“…Advanced mechanisms for scanning heritage assets aimed specifically at web applications (Dijkshoorn et al, 2018;Potenziani et al, 2018;Scopigno et al, 2017) corroborate the emergence of digital collections, promoting the online growth of complex and heterogeneous digital cultural objects. These objects largely originate from contemporary infrastructures such as digital repositories, information systems that capture, organize, semantically represent, store, manage, preserve, and supply digital content based on predefined procedures (Doerr et al, 2010;Siqueira & Martins, 2021). Araripe (2004) reports that cultural heritage is generally housed in libraries, archives, and museums and organized into information collections or documentary sources for research and teaching purposes.…”