GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs

Many knowledge graphs contain a substantial number of spatial entities, such as cities, buildings, and natural landmarks. For many of these entities, exact geometries are stored within the knowledge graphs. However, most existing approaches for learning entity representations do not take these geometries into account. In this paper, we introduce a variant of RDF2Vec that incorporates geometric information to learn location-aware embeddings of entities. Our approach expands different nodes by flooding the graph from geographic nodes, ensuring that each reachable node is considered. Based on the resulting flooded graph, we apply a modified version of RDF2Vec that biases graph walks using spatial weights. Through evaluations on multiple benchmark datasets, we demonstrate that our approach outperforms both non-location-aware RDF2Vec and GeoTransE.

A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid

Geospatial data plays a central role in modeling our world, for which OpenStreetMap (OSM) provides a rich source of such data. While often spatial data is represented in a tabular format, a graph based representation provides the possibility to interconnect entities which would have been separated in a tabular representation. We propose in our paper a framework which supports a planet scale transformation of OpenStreetMap data into a Spatial Temporal Knowledge Graph. In addition to OpenStreetMap data, we align the different OpenStreetMap geometries on individual h3 grid cells. We compare our constructed spatial knowledge graph to other spatial knowledge graphs and outline our contribution in this paper. As a basis for our computation, we use Apache Sedona as a computational framework for our Spatial Temporal Knowledge Graph construction