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Data Commons

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Data Commons is an open-source platform[1] created by Google[2] that provides an open knowledge graph, combining economic, scientific and other public datasets into a unified view.[3] Ramanathan V. Guha, a creator of web standards including RDF,[4] RSS, and Schema.org,[5] founded the project.[6]

The Data Commons website was launched in May 2018 with an initial dataset consisting of fact-checking data published in Schema.org "ClaimReview" format by several fact checkers from the International Fact-Checking Network.[7][8] Google has worked with partners such as the United Nations (UN) to populate the repository,[2] which also includes data from the United States Census, the World Bank, the US Bureau of Labor Statistics,[9] Wikipedia, the National Oceanic and Atmospheric Administration and the Federal Bureau of Investigation.[10]

The service expanded during 2019 to include an RDF-style knowledge graph populated from a number of largely statistical open datasets. The service was announced to a wider audience in 2019.[11] In 2020 the service improved its coverage of non-US datasets, while also increasing its coverage of bioinformatics and coronavirus.[12]

Features

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Data Commons places more emphasis on statistical data than is common for linked data and knowledge graph initiatives. It includes geographical, demographic, weather and real estate data alongside other categories,[3] describing states, Congressional districts, and cities in the United States as well as biological specimens, power plants, and elements of the human genome via the Encyclopedia of DNA Elements (ENCODE) project.[10] It represents data as semantic triples each of which can have its own provenance.[3] It centers on the entity-oriented integration of statistical observations from a variety of public datasets. Although it supports a subset of the W3C SPARQL query language,[13] its APIs[14] also include tools — such as a Pandas dataframe interface — oriented towards data science, statistics and data visualization.

Data Commons is integrative, meaning that, rather than providing a hosting platform for diverse datasets, it attempts to consolidate much of the information the datasets provide into a single data graph.

Technology

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Data Commons is built on a graph data-model. The graph can be accessed through a browser interface and several APIs,[3][10] and is expanded through loading data (typically CSV and MCF-based templates).[15] The graph can be accessed by natural language queries in Google Search.[16] The data vocabulary used to define the datacommons.org graph is based upon Schema.org.[3] In particular the Schema.org terms StatisticalPopulation[17] and Observation[18] were proposed to Schema.org to support datacommons-like usecases.[19]

Software from the project is available on GitHub under Apache 2 license.[20]

References

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  1. ^ "Custom Data Commons". Docs - Data Commons. Retrieved 16 July 2024.
  2. ^ a b "Data Commons is using AI to make the world's public data more accessible and helpful". Google. 13 September 2023. Retrieved 16 July 2024.
  3. ^ a b c d e Fensel, Dieter; Şimşek, Umutcan; Angele, Kevin; Huaman, Elwin; Kärle, Elias; Panasiuk, Oleksandra; Toma, Ioan; Umbrich, Jürgen; Wahler, Alexander (2020), "Introduction: What Is a Knowledge Graph?", Knowledge Graphs, Cham: Springer International Publishing, pp. 1–10, doi:10.1007/978-3-030-37439-6_1, ISBN 978-3-030-37438-9, S2CID 213620389, retrieved 2020-10-16
  4. ^ Guns, Raf (2013). "Tracing the origins of the semantic web". Journal of the American Society for Information Science and Technology. 64 (10): 2173–2181. doi:10.1002/asi.22907. hdl:10067/1111170151162165141.
  5. ^ Funke, Daniel (7 December 2017). "This website helps you find related fact checks - and it was built by a 17-year-old". Poynter. Retrieved 16 July 2024.
  6. ^ Guha, Ramanathan V. (15 October 2020). "Data Commons, now accessible on Google Search". docs.datacommons.org. Retrieved 2020-10-16.
  7. ^ "Fact Checks". datacommons.org. 29 March 2019. Retrieved 14 October 2020.
  8. ^ Jiang, Shan; Baumgartner, Simon; Ittycheriah, Abe; Yu, Cong (2020-04-20). "Factoring Fact-Checks: Structured Information Extraction from Fact-Checking Articles". Proceedings of the Web Conference 2020. WWW '20. Taipei Taiwan: ACM. pp. 1592–1603. doi:10.1145/3366423.3380231. ISBN 978-1-4503-7023-3. S2CID 215882520.
  9. ^ Raghavan, Prabhakar (2020-10-15). "How AI is powering a more helpful Google". Google. Retrieved 2020-10-16.
  10. ^ a b c Sheth, Amit; Padhee, Swati; Gyrard, Amelie; Sheth, Amit (2019-07-01). "Knowledge Graphs and Knowledge Networks: The Story in Brief". IEEE Internet Computing. 23 (4): 67–75. arXiv:2003.03623. doi:10.1109/MIC.2019.2928449. ISSN 1089-7801. S2CID 204820800.
  11. ^ Luong, Daphne; Chou, Charina (5 March 2019). "Doing our part to share open data responsibly". The Keyword. Retrieved 14 October 2020.
  12. ^ Ramasubramanian, Sowmya (21 September 2020). "Google's open source data to study impact of COVID-19". The Hindu. Retrieved 14 October 2020.
  13. ^ "Query the Data Commons Knowledge Graph using SPARQL". datacommons.org. Retrieved 14 October 2020.
  14. ^ "Overview". datacommons.org. Retrieved 14 October 2020.
  15. ^ "Contributing to Data Commons – Adding datasets". datacommons.org. Data Commons.
  16. ^ Guha, Ramanathan V. (15 October 2020). "Data Commons, now accessible on Google Search". docs.datacommons.org. Retrieved 2020-10-16.
  17. ^ "StatisticalPopulation type at Schema.org". schema.org. Retrieved 14 October 2020.
  18. ^ "Observation type at Schema.org". schema.org. Retrieved 14 October 2020.
  19. ^ "Proposal for representing Aggregate Statistical Data". GitHub – Schema.org repository. 25 June 2019. Retrieved 14 October 2020.
  20. ^ "datacommons.org GitHub". GitHub.
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