Skip to main content
SpringerLink
Log in
Book cover

International Semantic Web Conference

ISWC 2021: The Semantic Web – ISWC 2021 pp 429–445Cite as

CKGG: A Chinese Knowledge Graph for High-School Geography Education and Beyond

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12922))

Abstract

As part of a long-term research effort to provide students with better computer-aided education, we create CKGG, a Chinese knowledge graph for the geography domain at the high school level. Using GeoNames and Wikidata as a basis, we transform and integrate various kinds of geographical data in different formats from diverse sources, including gridded temperature data in NetCDF, precipitation data in HDF5, solar radiation data in AAIGrid, polygon data in GPKG, climate and ocean current data in images, and government data in tables. The current version of CKGG contains 1.5 billion triples and is accessible as Linked Data. We also publish a reified version for provenance tracking. We illustrate the potential application of CKGG with a prototype.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://www.geonames.org/.

  2. 2.

    https://w3id.org/ckgg/1.0/.

  3. 3.

    https://github.com/nju-websoft/CKGG.

  4. 4.

    wgs84_pos: http://www.w3.org/2003/01/geo/wgs84_pos#.

  5. 5.

    https://github.com/BYVoid/OpenCC.

  6. 6.

    http://berkeleyearth.lbl.gov/auto/Global/Gridded/Land_and_Ocean_Alternate_LatLong1.nc.

  7. 7.

    https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGM_06/summary.

  8. 8.

    https://api.globalsolaratlas.info/download/World/World_GHI_GISdata_LTAy_AvgDailyTotals_GlobalSolarAtlas-v2_AAIGRID.zip.

  9. 9.

    http://naciscdn.org/naturalearth/packages/natural_earth_vector.gpkg.zip.

  10. 10.

    https://commons.wikimedia.org/wiki/File:Corrientes-oceanicas.png.

  11. 11.

    http://www.stats.gov.cn/tjsj/tjbz/tjyqhdmhcxhfdm/2020/.

  12. 12.

    https://data.stats.gov.cn/adv.htm?cn=E0103.

  13. 13.

    https://github.com/jcklie/wikimapper.

  14. 14.

    https://public.ukp.informatik.tu-darmstadt.de/wikimapper/.

  15. 15.

    https://dumps.wikimedia.org/.

  16. 16.

    https://github.com/jodaiber/Annotated-WikiExtractor.

  17. 17.

    https://doi.org/10.5281/zenodo.4668711.

  18. 18.

    https://doi.org/10.5281/zenodo.4678089.

  19. 19.

    https://w3id.org/ckgg/1.0/demo/.

References

  1. Che, W., Feng, Y., Qin, L., Liu, T.: N-LTP: a open-source neural Chinese language technology platform with pretrained models. CoRR abs/2009.11616 (2020)

    Google Scholar 

  2. Chen, J., Deng, S., Chen, H.: CrowdGeoKG: crowdsourced geo-knowledge graph. In: Li, J., Zhou, M., Qi, G., Lao, N., Ruan, T., Du, J. (eds.) CCKS 2017. CCIS, vol. 784, pp. 165–172. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-7359-5_17

    Chapter  Google Scholar 

  3. Cheng, G., Xu, D., Qu, Y.: Summarizing entity descriptions for effective and efficient human-centered entity linking. In: WWW 2015, pp. 184–194 (2015). https://doi.org/10.1145/2736277.2741094

  4. Faria, D., Pesquita, C., Santos, E., Palmonari, M., Cruz, I.F., Couto, F.M.: The AgreementMakerLight ontology matching system. In: Meersman, R., et al. (eds.) OTM 2013. LNCS, vol. 8185, pp. 527–541. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41030-7_38

    Chapter  Google Scholar 

  5. Gunaratna, K., Yazdavar, A.H., Thirunarayan, K., Sheth, A.P., Cheng, G.: Relatedness-based multi-entity summarization. In: IJCAI 2017, pp. 1060–1066 (2017). https://doi.org/10.24963/ijcai.2017/147

  6. Hu, W., et al.: Clinga: bringing Chinese physical and human geography in linked open data. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 104–112. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_11

    Chapter  Google Scholar 

  7. Huang, Z., et al.: GeoSQA: a benchmark for scenario-based question answering in the geography domain at high school level. In: EMNLP-IJCNLP 2019, pp. 5865–5870 (2019). https://doi.org/10.18653/v1/D19-1597

  8. Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015). https://doi.org/10.3233/SW-140134

    Article  Google Scholar 

  9. Li, X., Sun, Y., Cheng, G.: TSQA: tabular scenario based question answering. In: AAAI 2021 (2021)

    Google Scholar 

  10. Liu, Q., Chen, Y., Cheng, G., Kharlamov, E., Li, J., Qu, Y.: Entity summarization with user feedback. In: Harth, A., et al. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 376–392. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49461-2_22

    Chapter  Google Scholar 

  11. Liu, Q., Cheng, G., Gunaratna, K., Qu, Y.: Entity summarization: state of the art and future challenges. J. Web Semant. 69, 100647 (2021). https://doi.org/10.1016/j.websem.2021.100647

    Article  Google Scholar 

  12. Liu, W., et al.: K-BERT: enabling language representation with knowledge graph. In: AAAI 2020, pp. 2901–2908 (2020)

    Google Scholar 

  13. Noy, N.F., McGuinness, D.L.: Ontology development 101: a guide to creating your first ontology. Technical report, KSL-01-05, Stanford University (2001)

    Google Scholar 

  14. Vrandecic, D., Krötzsch, M.: Wikidata: a free collaborative knowledge base. Commun. ACM 57(10), 78–85 (2014). https://doi.org/10.1145/2629489

    Article  Google Scholar 

  15. Wang, S., Zhang, X., Ye, P., Du, M., Lu, Y., Xue, H.: Geographic knowledge graph (GeoKG): a formalized geographic knowledge representation. ISPRS Int. J. Geo Inf. 8(4), 184 (2019). https://doi.org/10.3390/ijgi8040184

    Article  Google Scholar 

  16. Yang, A., et al.: Enhancing pre-trained language representations with rich knowledge for machine reading comprehension. In: ACL 2019, vol. 1, pp. 2346–2357 (2019). https://doi.org/10.18653/v1/p19-1226

  17. Zhang, Z., et al.: Towards answering geography questions in gaokao: a hybrid approach. In: Zhao, J., Harmelen, F., Tang, J., Han, X., Wang, Q., Li, X. (eds.) CCKS 2018. CCIS, vol. 957, pp. 1–13. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-3146-6_1

    Chapter  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018YFB1005100).

Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Yulin Shen, Ziheng Chen, Gong Cheng & Yuzhong Qu

Authors
  1. Yulin Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gong Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuzhong Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gong Cheng .

Editor information

Editors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Andreas Hotho

  2. Linköping University, Linköping, Sweden

    Eva Blomqvist

  3. University of Düsseldorf, Düsseldorf, Germany

    Stefan Dietze

  4. IBM Research - Thomas J. Watson Research, Hawthorne, CA, USA

    Achille Fokoue

  5. University of Texas, Austin, TX, USA

    Ying Ding

  6. Imperial College, London, UK

    Payam Barnaghi

  7. Australian National University, Canberra, ACT, Australia

    Armin Haller

  8. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  9. The Open University Walton Hall, Milton Keynes, UK

    Harith Alani

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shen, Y., Chen, Z., Cheng, G., Qu, Y. (2021). CKGG: A Chinese Knowledge Graph for High-School Geography Education and Beyond. In: Hotho, A., et al. The Semantic Web – ISWC 2021. ISWC 2021. Lecture Notes in Computer Science(), vol 12922. Springer, Cham. https://doi.org/10.1007/978-3-030-88361-4_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-88361-4_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88360-7

  • Online ISBN: 978-3-030-88361-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation