Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs

TL;DR

This paper introduces a neural method for aligning OpenStreetMap entities with knowledge graph classes, significantly improving semantic annotation coverage and accuracy by jointly considering schema and instance layers.

Contribution

It presents a novel neural architecture that leverages a shared latent space for schema alignment, outperforming existing methods and enabling extensive semantic enrichment of OSM data.

Findings

Outperforms state-of-the-art schema alignment methods by up to 53 F1-score points.

Enables semantic annotations for over 10 million OSM entities worldwide.

Increases existing semantic annotations in OSM by over 400%.

Abstract

OpenStreetMap (OSM) is one of the richest openly available sources of volunteered geographic information. Although OSM includes various geographical entities, their descriptions are highly heterogeneous, incomplete, and do not follow any well-defined ontology. Knowledge graphs can potentially provide valuable semantic information to enrich OSM entities. However, interlinking OSM entities with knowledge graphs is inherently difficult due to the large, heterogeneous, ambiguous, and flat OSM schema and the annotation sparsity. This paper tackles the alignment of OSM tags with the corresponding knowledge graph classes holistically by jointly considering the schema and instance layers. We propose a novel neural architecture that capitalizes upon a shared latent space for tag-to-class alignment created using linked entities in OSM and knowledge graphs. Our experiments performed to align OSM datasets for several countries with two of the most prominent openly available knowledge graphs, namely, Wikidata and DBpedia, demonstrate that the proposed approach outperforms the state-of-the-art schema alignment baselines by up to 53 percentage points in terms of F1-score. The resulting alignment facilitates new semantic annotations for over 10 million OSM entities worldwide, which is more than a 400% increase compared to the existing semantic annotations in OSM.