Building spatio-temporal knowledge graphs from vectorized topographic historical maps

Semantic web

article

Basel Shbita¹  Craig A. Knoblock¹  Weiwei Duan²  Yao-Yi Chiang²  Johannes H. Uhl³  Stefan Leyk³ 

[1] Information Sciences Institute, University of Southern California;Spatial Sciences Institute, University of Southern California;Department of Geography, University of Colorado Boulder

关键词: Spatio-temporal Knowledge Graphs;    Knowledge Graphs;    linked spatio-temporal data;    linked data;    Semantic Web;    data integration;    historical maps;    digital humanities;   

DOI  :  10.3233/SW-222918

来源: IOS Press

【 摘 要 】

Historical maps provide rich information for researchers in many areas, including the social and natural sciences. These maps contain detailed documentation of a wide variety of natural and human-made features and their changes over time, such as changes in transportation networks or the decline of wetlands or forest areas. Analyzing changes over time in such maps can be labor-intensive for a scientist, even after the geographic features have been digitized and converted to a vector format. Knowledge Graphs (KGs) are the appropriate representations to store and link such data and support semantic and temporal querying to facilitate change analysis. KGs combine expressivity, interoperability, and standardization in the Semantic Web stack, thus providing a strong foundation for querying and analysis. In this paper, we present an automatic approach to convert vector geographic features extracted from multiple historical maps into contextualized spatio-temporal KGs. The resulting graphs can be easily queried and visualized to understand the changes in different regions over time. We evaluate our technique on railroad networks and wetland areas extracted from the United States Geological Survey (USGS) historical topographic maps for several regions over multiple map sheets and editions. We also demonstrate how the automatically constructed linked data (i.e., KGs) enable effective querying and visualization of changes over different points in time.

【 授权许可】

Unknown