Sasha Bruns: The Persistence of Temporality: Tracing Time in Cultural Heritage Knowledge Graphs

Cultural heritage knowledge graphs (KGs) serve as invaluable tools for representing and understanding the meaningful connections among people, artifacts, practices, events, and traditions, shedding light on our shared heritage. By providing a structured semantic representation of cultural heritage data, KGs enhance interoperability, enabling global access and facilitating data reuse. Incorporating temporal context into cultural heritage KGs is crucial for a comprehensive understanding of historical events, their interconnections, and their influence on the present. Moreover, temporal integration allows for innovative exploration of diverse historical sources, objects, and artifacts from various periods, leveraging the power of semantic interlinking. Nevertheless, accurately representing, reasoning over, and querying temporal data in KGs present significant challenges.

Although RDF is a widely adopted representation language for encoding data on the Web and constructing KGs, the standard RDF model has inherent limitations in representing temporal aspects. Numerous approaches have been proposed to address the integration of time into RDF, varying in terms of the dimensions of time they adapt, the type of temporal data they consider, and how they express temporal statements. However, the selection of an appropriate modeling approach is highly dependent on the specific requirements of the use case. Hence, this talk aims to address and discuss two main questions within the context of cultural heritage KGs.

Firstly, it explores the temporal information that is crucial in the cultural heritage domain and deduces the requirements derived from several cultural heritage use cases. This investigation will contribute to an improved organization and comprehension of historical data by cultural heritage domain experts, allowing them to construct meaningful events, timelines, and narratives. Secondly, the talk investigates the current best practices for representing temporal information in RDF and highlights the most effective approaches for modeling time within RDF. Evaluating these practices in the context of cultural heritage use cases provides insights into their advantages, limitations, and suitability for representing temporal data. By thoroughly assessing these practices, we can establish guidelines and criteria for selecting the most appropriate modeling approach in the cultural heritage domain.

Answering these research questions will have far-reaching impacts on the cultural heritage field and beyond. The benefits include not only an enhanced organization and understanding of historical data by cultural heritage experts but also the potential for new insights and discoveries about the past. Furthermore, a structured representation of temporal information in RDF can drive the development of new digital tools, such as analytical tools for studying historical events or visualization tools for creating interactive timelines. These advancements will promote the representation and exploration of historical processes, such as court proceedings, and facilitate interoperability within heterogeneous tangible and intangible cultural data. Ultimately, this research will contribute to fostering public engagement with cultural heritage and encouraging cross-disciplinary collaboration among researchers, thereby enriching our collective understanding of our diverse and evolving human history.

Lecture within the Data for History Lectures series
Time: Wednesday, 21 June 2023, 4.15-5.45 pm
Place: via Zoom – Request access via email (digitalhistory@hu-berlin.de)

Contributed image:
Generated with Artbot “Draw a picture in the style of Salvador Dali which shows various people from different times, different geographies, different cultures”



Diesen Blogbeitrag zitieren
Digital History Berlin (Redaktion) (2023, 19. Juni). Sasha Bruns: The Persistence of Temporality: Tracing Time in Cultural Heritage Knowledge Graphs. Digital History Berlin. Abgerufen am 20. Juni 2024, von https://doi.org/10.58079/nl4q

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