The GeoLink knowledge graph

Cheatham, Michelle; Krisnadhi, Adila; Amini, Reihaneh; Hitzler, Pascal; Janowicz, Krzysztof; Shepherd, Adam; Narock, T. W.; Jones, Matthew B.

doi:10.1080/20964471.2018.1469291

Cited by 25 publications

(12 citation statements)

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“…In OneGeology-Europe (Laxton, 2017), multilingual vocabularies were developed for rock age and type, and were used to support federated data queries sent to map services in different languages. In the EarthCube Geolink project (Krisnadhi et al, 2015;Cheatham et al, 2018), the method of ontology design patterns was used to develop a modular ontology to support data integration from seven geoscience data repositories. The Google Dataset Search was released in 2018.…”

Section: Knowledge Graphs and Fair Datamentioning

confidence: 99%

Knowledge graph construction and application in geosciences: A review

Ma¹

2022

Preprint

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Knowledge graph (KG) is a topic of great interests to geoscientists as it can be deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless, comparing with the large amounts of publications on machine learning applications in geosciences, summaries and reviews of geoscience KGs are still limited. The aim of this paper is to present a comprehensive review of KG construction and implementation in geosciences. It consists of four major parts: 1) concepts rele-vant to KG and approaches for KG construction, 2) KG application in data collection, curation, and service, 3) KG application in data analysis, and 4) challenges and trends of geoscience KG creation and application in the near future. For each of the first three parts, a list of concepts, exemplar stud-ies, and best practices are summarized. Those summaries are synthesized together in the challenge and trend analyses. As artificial intelligence and data science are thriving in geosciences, we hope this review of geoscience KGs can be of value to practitioners in data-intensive geoscience studies.

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Section: Knowledge Graphs and Fair Datamentioning

confidence: 99%

Knowledge graph construction and application in geosciences: A review

Ma¹

2022

Preprint

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“…In the US, several geospatial linked data projects have been conducted: a pilot of design and development of linked data from The National Map was performed [26]; the Geographic Names Information System was served as linked data, and its geospatial visualization was enabled [20]; the GeoLink knowledge graph was published following linked data principles and served through a SPARQL endpoint, including Earth Science information captured by oceanographic cruises, physical sample metadata, etc. [27]. Along with these linked data, development endeavors from authorities, crowd-sourcing projects have also produced several geospatial linked datasets, and some of them are serving as central hubs of the LOD cloud, e.g., GeoNames (https://www.geonames.org/) and LinkedGeoData (a linked data distribution of OpenStreetMap [28]).…”

Section: Geospatial Semantic Web and Linked Datamentioning

confidence: 99%

Assessment and Benchmarking of Spatially Enabled RDF Stores for the Next Generation of Spatial Data Infrastructure

Huang

Raza

Mirzov

et al. 2019

IJGI

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Geospatial information is indispensable for various real-world applications and is thus a prominent part of today’s data science landscape. Geospatial data is primarily maintained and disseminated through spatial data infrastructures (SDIs). However, current SDIs are facing challenges in terms of data integration and semantic heterogeneity because of their partially siloed data organization. In this context, linked data provides a promising means to unravel these challenges, and it is seen as one of the key factors moving SDIs toward the next generation. In this study, we investigate the technical environment of the support for geospatial linked data by assessing and benchmarking some popular and well-known spatially enabled RDF stores (RDF4J, GeoSPARQL-Jena, Virtuoso, Stardog, and GraphDB), with a focus on GeoSPARQL compliance and query performance. The tests were performed in two different scenarios. In the first scenario, geospatial data forms a part of a large-scale data infrastructure and is integrated with other types of data. In this scenario, we used ICOS Carbon Portal’s metadata—a real-world Earth Science linked data infrastructure. In the second scenario, we benchmarked the RDF stores in a dedicated SDI environment that contains purely geospatial data, and we used geospatial datasets with both crowd-sourced and authoritative data (the same test data used in a previous benchmark study, the Geographica benchmark). The assessment and benchmarking results demonstrate that the GeoSPARQL compliance of the RDF stores has encouragingly advanced in the last several years. The query performances are generally acceptable, and spatial indexing is imperative when handling a large number of geospatial objects. Nevertheless, query correctness remains a challenge for cross-database interoperability. In conclusion, the results indicate that the spatial capacity of the RDF stores has become increasingly mature, which could benefit the development of future SDIs.

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“…GeoLink (Narock et al, 2014;Krisnadhi et al, 2015;Cheatham et al, 2018) is a collection of Linked Open Data that addresses scholarly discovery and collaboration in the geosciences. GeoLink leverages the Semantic Web to publish open data regarding data centers, digital repositories, libraries, and professional societies.…”

Section: Steps Toward Connections To Other Networkmentioning

confidence: 99%

“…GeoLink leverages the Semantic Web to publish open data regarding data centers, digital repositories, libraries, and professional societies. One component of the GeoLink knowledge graph (Cheatham et al, 2018) is a collection of all National Science Foundation (NSF) funded projects. Figure 14 (reproduced from Narock and Wimmer, 2017) illustrates what can be done when one network is connected to another.…”

Section: Steps Toward Connections To Other Networkmentioning

confidence: 99%