  Historical maps contain rich geographic information about the past of a
region. They are sometimes the only source of information before the
availability of digital maps. Despite their valuable content, it is often
challenging to access and use the information in historical maps, due to their
forms of paper-based maps or scanned images. It is even more time-consuming and
labor-intensive to conduct an analysis that requires a synthesis of the
information from multiple historical maps. To facilitate the use of the
geographic information contained in historical maps, one way is to build a
geographic knowledge graph (GKG) from them. This paper proposes a general
workflow for completing one important step of building such a GKG, namely
aligning the same geographic entities from different maps. We present this
workflow and the related methods for implementation, and systematically
evaluate their performances using two different datasets of historical maps.
The evaluation results show that machine learning and deep learning models for
matching place names are sensitive to the thresholds learned from the training
data, and a combination of measures based on string similarity, spatial
distance, and approximate topological relation achieves the best performance
with an average F-score of 0.89.
