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However, one of the most critical limitations of geographic knowledge graphs is the lack of semantic relations between geographic entities. The most extensive knowledge graphs specifically tailored to geographic entities are extracted from unstructured sources, with these graphs often relying on datatype properties to describe the entities, resulting in a flat representation that lacks entity relationships. Therefore, predicting links between geographic entities is essential for advancing semantic geospatial applications. Existing neural link prediction methods for knowledge graphs typically rely on pre-existing entity relations, making them unsuitable for scenarios where such information is absent. In this paper, we tackle the challenge of predicting spatial links in sparsely interlinked knowledge graphs by introducing two novel approaches: supervised spatial link prediction (SSLP) and unsupervised inductive spatial link prediction (USLP). These approaches leverage the wealth of literal values in geographic knowledge graphs through spatial and semantic embeddings. To assess the effectiveness of our proposed methods, we conduct evaluations on the WorldKG geographic knowledge graph, which incorporates geospatial data extracted from OpenStreetMap. Our results demonstrate that the SSLP and USLP approaches substantially outperform state-of-the-art link prediction methods.<\/jats:p>","DOI":"10.1007\/978-3-031-47240-4_10","type":"book-chapter","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T08:02:40Z","timestamp":1698825760000},"page":"179-196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Spatial Link Prediction with\u00a0Spatial and\u00a0Semantic Embeddings"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2539-9277","authenticated-orcid":false,"given":"Genivika","family":"Mann","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5884-6234","authenticated-orcid":false,"given":"Alishiba","family":"Dsouza","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1619-3164","authenticated-orcid":false,"given":"Ran","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5134-9072","authenticated-orcid":false,"given":"Elena","family":"Demidova","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Ahmadi, N., Huynh, V., Meduri, V.V., Ortona, S., Papotti, P.: Mining expressive rules in knowledge graphs. 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