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Abstract

Cultural heritage institutions are publishing their digital collections over the web as LOD. This is is a new step in the patrimonialization and curatorial processes developed by such institutions. Many of these collections are thematically superimposed and complementary. Frequently, objects in these collections present culturally relevant relationships, such as a book about a painting, or a draft or sketch of a famous painting, etc. LOD technology enables such heritage records to be interlinked, achieving interoperability and adding value to digital collections, thus empowering heritage institutions. An aim of this research is characterizing such culturally relevant relationships and organizing them in a vocabulary. Use cases or examples of relationships between objects suggested by curators or mentioned in literature and in the conceptual models as FRBR/LRM, CIDOC CRM and RiC-CM, were collected and used as examples or inspiration of cultural relevant relationships. Relationships identified are collated and compared for identifying those with the same or similar meaning, synthesized and normalized. A set of thirty-three culturally relevant relationships are identified and formalized as a LOD property vocabulary to be used by digital curators to interlink digital collections. The results presented are provisional and a starting point to be discussed, tested, and enhanced.

Date

4. 3.2020 14:22:41

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Abstract

The objective of this work is to discuss how vocabularies can contribute to assigning computational semantics to digital research data within the context of Big Data, so that computers can process them, allowing their reuse on large scale. A conceptualization of data is developed in an attempt to make it clearer what would be data, as an essential element of the Big Data phenomenon, and in particular, digital research data. It then proceeds to analyse digital research data uses and cases and their relation to semantics and vocabularies. Data is conceptualized as an artificial, intentional construction that represents a property of an entity within a specific domain and serves as the essential component of Big Data. The concept of semantic expressivity is discussed, and is used to classify the different vocabularies; within such a classification ontologies, are shown to be a type of knowledge organization system with a higher degree of semantic expressivity. Features of vocabularies that may be used within the context of the Semantic Web and the Linked Open Data to assign machine-processable semantics to Big Data are suggested. It is shown that semantics may be assigned at different data aggregation levels.