Towards Leveraging Link Prediction for Geospatial Data Integration

Proc. of the 33rd Italian Symp. on Advanced Database Systems (SEBD). CEUR Workshop Proceedings, https://ceur-ws.org/. 2025.

Link prediction is a technique used to predict new relationships between entities in a given graph. There are several domains in which this technique is applied. Those span from social media friendship links suggestions to correlated products prediction. Nevertheless, the use of link prediction to support knowledge integration is still a subject of debate, especially in the context of geospatial data. In this paper, we aim to discuss the role and some of the potential benefits of link prediction in the context of geospatial data completion and integration. To this end, we aim to position and discuss the role of geospatial link prediction within the framework of Ontology-Based Data Access (OBDA), highlighting the potential contribution of link prediction in this field. Additionally, we present a series of preliminary experiments designed to predict relationships of "competition" among business activities within a specific geographic area. Finally, we explore how the injection of knowledge from information-rich schemas about concepts related to the geospatial domain can positively influence the accuracy of the prediction model.

@inproceedings{SEBD-2025-links,
   title = "Towards Leveraging Link Prediction for Geospatial Data
Integration",
   year = "2025",
   author = "Albulen Pano and Mattia Fumagalli and Davide Lanti and Diego
Calvanese",
   booktitle = "Proc. of the 33rd Italian Symp. on Advanced Database Systems
(SEBD)",
   publisher = "CEUR-WS.org",
   series = "CEUR Workshop Proceedings, https://ceur-ws.org/",
}