Extending the YAGO2 Knowledge Graph with Precise Geospatial Knowledge

Karalis, Nikolaos; Mandilaras, Georgios M.; Koubarakis, Manolis

doi:10.1007/978-3-030-30796-7_12

Cited by 23 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Code: The code realizing the WorldKG creation process depicted in Figure 3 is available on GitHub 16 under the MIT License 17 . • Data: The WorldKG triples can be downloaded from a persistent URL 18 under the Open Data Commons Open Database License (ODbL) 19 . On the WorldKG website, we also made the manually created evaluation dataset of owl:equi-valentClass mappings between the WorldKG ontology and the DBpedia/Wikidata ontologies available.…”

Section: Availabilitymentioning

confidence: 99%

“…However, both DBpedia and Wikidata only cover a small fraction of OSM locations [32]. YAGO2geo [19] is an extension of the YAGO knowledge graph that includes geospatial and temporal relations. YAGO2geo is created using OSM and reference geospatial datasets such as Greek Administrative Geography (GAG) and Global Administrative Areas dataset (GADM).…”

Section: Kgs Containing Geographic Entitiesmentioning

confidence: 99%

“…However, popular general-purpose knowledge graphs such as DBpedia and Wikidata lack coverage of geographic entities [32]. In contrast, specialized geographic knowledge graphs such as LinkedGeoData [3] and YAGO2geo [19] lack coverage of geographic classes. To provide a comprehensive source of semantic geographic information at scale, semantic information in knowledge graphs and community-created geographic sources such as OSM should be tightly integrated and fused.…”

Section: Introductionmentioning

confidence: 99%

“…WorldKG Website: http://www.worldkg.org/16 GitHub Link: https://github.com/alishiba14/WorldKG-Knowledge-Graph17 License for code: https://opensource.org/licenses/MIT18 DOI for WorldKG data: https://doi.org/10.5281/zenodo.495398619 License for dataset: https://opendatacommons.org/licenses/odbl/…”

mentioning

confidence: 99%

See 3 more Smart Citations

WorldKG: A World-Scale Geographic Knowledge Graph

Dsouza,

Tempelmeier,

et al. 2021

Preprint

View full text Add to dashboard Cite

OpenStreetMap is a rich source of openly available geographic information. However, the representation of geographic entities, e.g., buildings, mountains, and cities, within OpenStreetMap is highly heterogeneous, diverse, and incomplete. As a result, this rich data source is hardly usable for real-world applications. This paper presents WorldKG -a new geographic knowledge graph aiming to provide a comprehensive semantic representation of geographic entities in OpenStreetMap. We describe the WorldKG knowledge graph, including its ontology that builds the semantic dataset backbone, the extraction procedure of the ontology and geographic entities from OpenStreetMap, and the methods to enhance entity annotation. We perform statistical and qualitative dataset assessment, demonstrating the large scale and high precision of the semantic geographic information in WorldKG.

show abstract

Section: Availabilitymentioning

confidence: 99%

Section: Kgs Containing Geographic Entitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

WorldKG: A World-Scale Geographic Knowledge Graph

Dsouza,

Tempelmeier,

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, the query GeorgeHarrison created ?s after JohnLennon would find songs written by George Harrison after John Lennon's death, and the query guitarists bornIn ?p near Seattle would return Jimi Hendrix, Kurt Cobain, Carla Torgerson and more. Recent work on augmenting YAGO2 with spatial knowledge is the Yago2geo project [276], which integrated content from OpenStreetMap and other sources, and supports a very expressive query language called GeoSPARQL.…”

Section: Submitted To Foundations and Trends In Databasesmentioning

confidence: 99%

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Weikum¹,

Dong²,

Razniewski³

et al. 2020

Preprint

View full text Add to dashboard Cite

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics.This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

show abstract

Scalable Transformation of Big Geospatial Data into Linked Data

Mandilaras

Koubarakis

2021

The Semantic Web – ISWC 2021

View full text Add to dashboard Cite

Extending the YAGO2 Knowledge Graph with Precise Geospatial Knowledge

Cited by 23 publications

References 14 publications

WorldKG: A World-Scale Geographic Knowledge Graph

WorldKG: A World-Scale Geographic Knowledge Graph

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Scalable Transformation of Big Geospatial Data into Linked Data

Contact Info

Product

Resources

About