%0 Conference Paper %1 10.1007/978-3-030-88361-4_4 %A Dsouza, Alishiba %A Tempelmeier, Nicolas %A Demidova, Elena %B The Semantic Web -- ISWC 2021 %C Cham %D 2021 %E Hotho, Andreas %E Blomqvist, Eva %E Dietze, Stefan %E Fokoue, Achille %E Ding, Ying %E Barnaghi, Payam %E Haller, Armin %E Dragoni, Mauro %E Alani, Harith %I Springer International Publishing %K KnowledgeGraph myown openstreetmap %P 56--73 %T Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs %X 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. %@ 978-3-030-88361-4

@inproceedings{10.1007/978-3-030-88361-4_4, 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 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.}, added-at = {2022-02-18T13:52:18.000+0100}, address = {Cham}, author = {Dsouza, Alishiba and Tempelmeier, Nicolas and Demidova, Elena}, biburl = {https://www.bibsonomy.org/bibtex/2e5b87ba2dae1399c5b632acae948c8e3/alishiba}, booktitle = {The Semantic Web -- ISWC 2021}, description = {Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs | SpringerLink}, editor = {Hotho, Andreas and Blomqvist, Eva and Dietze, Stefan and Fokoue, Achille and Ding, Ying and Barnaghi, Payam and Haller, Armin and Dragoni, Mauro and Alani, Harith}, interhash = {0d505821fe15c7de2878e041483b3d7a}, intrahash = {e5b87ba2dae1399c5b632acae948c8e3}, isbn = {978-3-030-88361-4}, keywords = {KnowledgeGraph myown openstreetmap}, pages = {56--73}, publisher = {Springer International Publishing}, timestamp = {2022-02-18T13:54:06.000+0100}, title = {Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs}, year = 2021 }