PeGazUs: A knowledge graph based approach to build urban perpetual gazetteers - Institut national de l’information géographique et forestière - Ecole nationale des sciences géographiques
Communication Dans Un Congrès Année : 2024

PeGazUs: A knowledge graph based approach to build urban perpetual gazetteers

Résumé

Gazetteers, as compilation of named places, are central re-sources on the Web of data, as they provide a common ground to link and integrate many textual or structured resources on the Web. Gazetteers usually categorise and associate places names with geospatial coordinates. In more recent times, historical gazetteers, which aim to represent places from the past, have received increasing attention. The creation of these gazetteers poses specific challenges, including the definition of the identity of evolving places, the representation of their evolution through time (how they change, when the changes happen), and the population of the gazetteer based on scarce and heterogeneous historical sources. We propose an approach to create an urban historical gazetteer on the evolution of two major urban large-scale types of places, namely addresses and land plots. Our proposal is inspired by approaches for creating knowledge graphs and takes advantage of the knowledge representation and reasoning possibilities offered by Semantic Web standards to address the aforementioned challenges. The approach was applied to the Butte aux Cailles district of Paris, for which a variety of contemporary and historical sources were used. The resulting knowledge graph can be used for a variety of purposes, including historical geocoding of old documents, identifying the use of a plot of land at a given date, and recording the events that led to its current state.
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Dates et versions

hal-04721538 , version 1 (22-11-2024)

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Charly Bernard, Solenn Tual, Nathalie Abadie, Bertrand Duménieu, Julien Perret, et al.. PeGazUs: A knowledge graph based approach to build urban perpetual gazetteers. International Conference on Knowledge Engineering and Knowledge Management (EKAW 2024), Nov 2024, Amsterdam, Netherlands. pp.364-381, ⟨10.1007/978-3-031-77792-9_22⟩. ⟨hal-04721538⟩
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