Audio Ontologies for Intangible Cultural Heritage

Cultural heritage portals often contain intangible objects digitized as audio files. This paper presents and discusses the adaptation of existing audio ontologies intended for non-cultural heritage applications. The resulting alignment of the German Digital Library-Europeana Data Model (DDB-EDM) with Music Ontology (MO) and Audio Commons Ontology (ACO) is presented

Knowledge Extraction for Art History: the Case of Vasari’s The Lives of The Artists (1568)

Knowledge Extraction (KE) techniques are used to convert unstructured information present in texts to Knowledge Graphs (KGs) which can be queried and explored. Despite their potential for cultural heritage domains, such as Art History, these techniques often encounter limitations if applied to domain-specific data. In this paper we present the main challenges that KE has to face on art-historical texts, by using as case study Giorgio Vasari's The Lives of The Artists. This paper discusses the following NLP tasks for art-historical texts, namely entity recognition and linking, coreference resolution, time extraction, motif extraction and artwork extraction. Several strategies to annotate art-historical data for these tasks and evaluate NLP models are also proposed

Multimodal Search on Iconclass using Vision-Language Pre-Trained Models

Terminology sources, such as controlled vocabularies, thesauri and classification systems, play a key role in digitizing cultural heritage. However, Information Retrieval (IR) systems that allow to query and explore these lexical resources often lack an adequate representation of the semantics behind the user's search, which can be conveyed through multiple expression modalities (e.g., images, keywords or textual descriptions). This paper presents the implementation of a new search engine for one of the most widely used iconography classification system, Iconclass. The novelty of this system is the use of a pre-trained vision-language model, namely CLIP, to retrieve and explore Iconclass concepts using visual or textual queries

Knowledge Extraction for Art History: the Case of Vasari’s The Lives of The Artists (1568)

Knowledge Extraction (KE) techniques are used to convert unstructured information present in texts to Knowledge Graphs (KGs) which can be queried and explored. Despite their potential for cultural heritage domains, such as Art History, these techniques often encounter limitations if applied to domain-specific data. In this paper we present the main challenges that KE has to face on art-historical texts, by using as case study Giorgio Vasari’s The Lives of The Artists. This paper discusses the following NLP tasks for art-historical texts, namely entity recognition and linking, coreference resolution, time extraction, motif extraction and artwork extraction. Several strategies to annotate art-historical data for these tasks and evaluate NLP models are also proposed

From Floppy Disks to 5-Star LOD: FAIR Research Infrastructure for NFDI4Culture

NFDI4Culture is establishing an infrastructure for research data on material and immaterial cultural heritage in the context of the German National Research Data Infrastructure (NFDI) in compliance with the FAIR principles. The NFDI4Culture Knowledge Graph is developed and integrated with the Culture Information Portal to aggregate diverse and isolated data from the culture research landscape and thereby increase the discoverability, interoperability and reusability of cultural heritage data. This paper presents the research data management strategy in the long-term project NFDI4Culture, which combines a CMS and a Knowledge Graph-based infrastructure to enable an intuitive and meaningful interaction with research resources in the cultural heritage domain

From Floppy Disks to 5-Star LOD: FAIR Research Infrastructure for NFDI4Culture

NFDI4Culture is establishing an infrastructure for research data on material and immaterial cultural heritage in the context of the German National Research Data Infrastructure (NFDI) in compliance with the FAIR principles. The NFDI4Culture Knowledge Graph is developed and integrated with the Culture Information Portal to aggregate diverse and isolated data from the culture research landscape and thereby increase the discoverability, interoperability and reusability of cultural heritage data. This paper presents the research data management strategy in the long-term project NFDI4Culture, which combines a CMS and a Knowledge Graph-based infrastructure to enable an intuitive and meaningful interaction with research resources in the cultural heritage domain

Communities, Harvesting, and CGIF: Building the Research Data Graph at NFDI4Culture

NFDI4Culture provides the Culture Knowledge Graph to facilitate FAIRness in its communities. It provides a lightweight infrastructure that interconnects research data from diverse domains such as architectural studies, art history, musicology, performing arts, and media studies. To allow further exploration of resources across individual knowledge silos, the graph is designed to be compatible with other research areas as well. It requires data providers to implement the Culture Graph Interchange Format, a subset of schema.org classes and properties, either as embedded metadata, an API, or a SPARQL endpoint that can be harvested, or as an RDF data dump. Connections between resources are established via controlled vocabularies, and ingested data is made available via the Culture Information Portal\u27s SPARQL endpoint. After introducing the infrastructural need, the paper reviews comparable solutions, describes the graph, discusses technical choices, and outlines our engagement within and beyond NFDI4Culture

Culture Graph Interchange Format Specification

For the federated acquisition of data for the Culture Knowledge Graph we propose an easy to use, lightweight interchange format based on schema.org for the harvesting of resources from data collections with key attributes (IRIs, names, dates and terms from controlled vocabularies). The Culture Graph Interchange Format (CGIF) has the added benefit of automatically making the data eligible for Google Dataset Search and to significantly improve the findability of websites and datasets through search engine optimization. This is the data publication (source files, figures, post-use materials) of the CGIF specification. The web version is available at https://nfdi4culture.de/go/E3712Funded by the German Research Foundation (DFG) – 441958017 (NFDI4Culture

From Floppy Disks to 5-Star LOD: FAIR Research Infrastructure for NFDI4Culture

NFDI4Culture is establishing an infrastructure for research data on material and immaterial cultural heritage in the context of the German National Research Data Infrastructure (NFDI) in compliance with the FAIR principles. The NFDI4Culture Knowledge Graph is developed and integrated with the Culture Information Portal to aggregate diverse and isolated data from the culture research landscape and thereby increase the discoverability, interoperability and reusability of cultural heritage data. This paper presents the research data management strategy in the long-term project NFDI4Culture, which combines a CMS and a Knowledge Graph-based infrastructure to enable an intuitive and meaningful interaction with research resources in the cultural heritage domain