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Defining and identifying duplicate records in a dataset is a challenging task which grows more complex when the modeled entities themselves are hard to delineate. In the geospatial domain, it may not be clear where a mountain, stream, or valley ends and begins, a problem carried over when such entities are catalogued in gazetteers. In this paper, we take two gazetteers, GeoNames and SwissNames3D, and perform matching - identifying records in each that are about the same entity - across a sample of natural feature records. We first perform rule-based matching, establishing competitive results, then apply machine learning using Random Forests, a method well-suited to the matching task. We report on the performance of a wider array of matching features than has been previously studied, including domain-specific ones such as feature type, land cover class, and elevation. Our results show an increase in performance using machine learning over rules, with a notable performance gain from considering feature types, but negligible gains from other specialized matching features. We argue that future work in this area should strive to be more reproducible and report results on a realistic testing pipeline including candidate selection, feature extraction, and classification.

We investigate gazetteer matching between natural features in Switzerland. We produce a gold standard dataset for a subset of features from 8 natural feature types in GeoNames aligned with their corresponding match(es) in SwissNames3D, an authoritative gazetteer. Based on this dataset, we comment on feature type alignments between the two resources and on type-specific differences to take into consideration for the matching task. We present preliminary results of rule-based matching and plans for future work.