Building spatio-Temporal knowledge graphs from vectorized topographic historical maps

Basel Shbita, Craig A. Knoblock, Weiwei Duan, Yao Yi Chiang, Johannes H. Uhl, Stefan Leyk

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)527-549
Number of pages23
JournalSemantic Web
Issue number3
StatePublished - 2023
Externally publishedYes

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant Nos. IIS 1564164 (to the University of Southern California) and IIS 1563933 (to the University of Colorado at Boulder) and Nvidia Corporation.

Publisher Copyright:
© 2023-The authors. Published by IOS Press.


  • data integration
  • digital humanities
  • historical maps
  • Knowledge Graphs
  • linked data
  • linked spatio-Temporal data
  • Semantic Web
  • Spatio-Temporal Knowledge Graphs


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