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From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards

  • Henrique SantosEmail author
  • Victor Dantas
  • Vasco Furtado
  • Paulo Pinheiro
  • Deborah L. McGuinness
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10250)

Abstract

In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.

References

  1. 1.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  2. 2.
    Biega, J., Kuzey, E., Suchanek, F.M.: Inside YAGO2s: a transparent information extraction architecture. In: Proceedings of the 22nd International Conference on World Wide Web Companion, WWW 2013 Companion, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 325–328 (2013)Google Scholar
  3. 3.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, NY, USA, pp. 1247–1250. ACM, New York (2008)Google Scholar
  4. 4.
    Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., Zhang, W.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, NY, USA, pp. 601–610. ACM, New York (2014)Google Scholar
  5. 5.
    Fox, M.S.: PolisGnosis Project: Representing and Analysing City Indicators. Working Paper, Enterprise Integration Laboratory, University of Toronto (2015). http://eil.utoronto.ca/wp-content/uploads/smartcities/papers/PolisGnosi.pdf
  6. 6.
    Fox, M.S.: The role of ontologies in publishing and analyzing city indicators. Comput. Environ. Urban Syst. 54, 266–279 (2015)CrossRefGoogle Scholar
  7. 7.
    Fox, P., McGuinness, D.L., Cinquini, L., West, P., Garcia, J., Benedict, J.L., Middleton, D.: Ontology-supported scientific data frameworks: the Virtual Solar-Terrestrial Observatory experience. Comput. Geosci. 35(4), 724–738 (2009)CrossRefGoogle Scholar
  8. 8.
    Hoffart, J., Suchanek, F.M., Berberich, K., Lewis-Kelham, E., de Melo, G., Weikum, G.: YAGO2: exploring and querying world knowledge in time, space, context, and many languages. In: Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, NY, USA, pp. 229–232. ACM, New York (2011)Google Scholar
  9. 9.
    ISO: Sustainable development of communities - Indicators for city services and quality of life. ISO 37120:2014, International Organization for Standardization, May 2014. http://www.iso.org/iso/catalogue_detail?csnumber=62436
  10. 10.
    McGuinness, D.L., Pinheiro, P., Santos, H.O., Klawonn, M., Chastain, K.: Semantic support for complex ecosystem research environments. In: AGU Fall Meeting Abstracts 33, December 2015Google Scholar
  11. 11.
    Pinheiro, P., McGuinness, D.L., Santos, H.: Human-aware sensor network ontology: semantic support for empirical data collection. In: Proceedings of the 5th Workshop on Linked Science, Bethlehem, PA, USA (2015)Google Scholar
  12. 12.
    Rew, R., Davis, G.: NetCDF: an interface for scientific data access. IEEE Comput. Graph. Appl. 10(4), 76–82 (1990)CrossRefGoogle Scholar
  13. 13.
    Santos, H., Furtado, V., Pinheiro, P., McGuinness, D.L.: Contextual data collection for smart cities. In: Proceedings of the Sixth Workshop on Semantics for Smarter Cities, Bethlehem, PA, USA (2015)Google Scholar
  14. 14.
    Singhal, A.: Introducing the Knowledge Graph: things, not strings (2012). https://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html
  15. 15.
    Zhao, J., Wang, Y.: Toward domain knowledge model for smart city: the core conceptual model. In: 2015 IEEE First International Smart Cities Conference (ISC2), pp. 1–5 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Henrique Santos
    • 1
    Email author
  • Victor Dantas
    • 1
  • Vasco Furtado
    • 1
  • Paulo Pinheiro
    • 2
  • Deborah L. McGuinness
    • 2
  1. 1.Universidade de FortalezaFortalezaBrazil
  2. 2.Rensselaer Polytechnic InstituteTroyUSA

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