Slides for the Abstra paper (EDBT'24)

Digital data sharing leads to unprecedented opportunities to develop data-driven systems for supporting economic activities, the social and political life, and science. Many open-access datasets are RDF (Linked Data) graphs, but others are JSON or XML documents, CSV files, Neo4J property graphs, etc.

International audience

Points of interest (POI) are central in many applications such as tourism, itinerary search, crisis management. Cartographic providers usually represent these POI with a spatial entity. However, the description of these entities may significantly vary from one provider to another (e.g., missing properties, outdated information, conflicting values). Spatial entity matching (or record linkage) aims at detecting correspondences between entities referring to the same POI. Most existing approaches have a fixed function for combining similarity measures, thus limiting customization. Besides, evaluating the matching quality is a difficult task since a ground truth dataset cannot be built for all entities and providers. In this paper, we describe GeoAlign, an application that allows fine-grained tuning for spatial entity matching. A merging step is also provided using different strategies. Finally, we propose to estimate the quality of correspondences based on the differences between combination functions and to visualize this estimation in GeoAlign.