TY - GEN
T1 - Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs
AU - Dsouza, Alishiba
AU - Tempelmeier, Nicolas
AU - Demidova, Elena
N1 - Funding Information:
Acknowledgements. This work was partially funded by DFG, German Research Foundation (“WorldKG”, DE 2299/2-1), BMBF, Germany (“Simple-ML”, 01IS18054) and BMWi, Germany (“d-E-mand”, 01ME19009B).
PY - 2021/9/30
Y1 - 2021/9/30
N2 - 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 aligning 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 37% points F1-score. The resulting alignment facilitates new semantic annotations for over 10 million OSM entities worldwide, which is over a 400% increase compared to the existing annotations.
AB - 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 aligning 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 37% points F1-score. The resulting alignment facilitates new semantic annotations for over 10 million OSM entities worldwide, which is over a 400% increase compared to the existing annotations.
KW - Knowledge graph
KW - Neural schema alignment
KW - OpenStreetMap
UR - https://www.scopus.com/pages/publications/85115872504
U2 - 10.48550/arXiv.2107.13257
DO - 10.48550/arXiv.2107.13257
M3 - Conference contribution
AN - SCOPUS:85115872504
SN - 9783030883607
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 56
EP - 73
BT - The Semantic Web
A2 - Hotho, Andreas
A2 - Blomqvist, Eva
A2 - Dietze, Stefan
A2 - Fokoue, Achille
A2 - Ding, Ying
A2 - Barnaghi, Payam
A2 - Haller, Armin
A2 - Dragoni, Mauro
A2 - Alani, Harith
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Semantic Web Conference, ISWC 2021
Y2 - 24 October 2021 through 28 October 2021
ER -