Linked Open Data as the Fuel for Smarter Cities

  • Mikel EmaldiEmail author
  • Oscar Peña
  • Jon Lázaro
  • Diego López-de-Ipiña
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 4)


In the last decade big efforts have been carried out in order to move towards the Smart City concept, from both the academic and industrial points of view, encouraging researchers and data stakeholders to find new solutions on how to cope with the huge amount of generated data. Meanwhile, Open Data has arisen as a way to freely share contents to be consumed without restrictions from copyright, patents or other mechanisms of control. Nowadays, Open Data is an achievable concept thanks to the World Wide Web, and has been re-defined for its application in different domains. Regarding public administrations, the concept of Open Government has found an ally in Open Data concepts, defending citizens’ right to access data, documentation and proceedings of the governments.

We propose the use of Linked Open Data , a set of best practices to publish data on the Web recommended by the W3C, in a new data life cycle management model, allowing governments and individuals to handle better their data, easing its consumption by anybody, including both companies and third parties interested in the exploitation of the data, and citizens as end users receiving relevant curated information and reports about their city. In summary, Linked Open Data uses the previous Openness concepts to evolve from an infrastructure thought for humans, to an architecture for the automatic consumption of big amounts of data, providing relevant and high-quality data to end users with low maintenance costs. Consequently, smart data can now be achievable in smart cities.


Resource Description Framework Link Data Smart City Link Open Data Resource Description Framework Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    World Health Organization: Urbanization and health. Bull World Health Organ. 88, 245–246 (2010)Google Scholar
  2. 2.
    Bizer, C., Boncz, P., Brodie, M.L., Erling, O.: The meaningful use of big data: four perspectives – four challenges. SIGMOD Rec. 40(4), 56–60 (2012)CrossRefGoogle Scholar
  3. 3.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284(5), 28–37 (2001)CrossRefGoogle Scholar
  4. 4.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. International Journal on Semantic Web and Information Systems (IJSWIS) 5(3), 1–22 (2009)CrossRefGoogle Scholar
  5. 5.
    Initiative, D.D.: Overview of the DDI version 3.0 conceptual model (April 2008)Google Scholar
  6. 6.
    Ball, A.: Review of data management lifecycle models (2012)Google Scholar
  7. 7.
    Burton, A., Treloar, A.: Designing for discovery and re-use: the ‘ANDS data sharing verbs’ approach to service decomposition. International Journal of Digital Curation 4(3), 44–56 (2009)CrossRefGoogle Scholar
  8. 8.
    Michener, W.K., Jones, M.B.: Ecoinformatics: supporting ecology as a data-intensive science. Trends in Ecology & Evolution 27(2), 85–93 (2012)CrossRefGoogle Scholar
  9. 9.
    Auer, S., et al.: Managing the life-cycle of linked data with the LOD2 stack. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part II. LNCS, vol. 7650, pp. 1–16. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    deRoos, D., Eaton, C., Lapis, G., Zikopoulos, P., Deutsch, T.: Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media (2011)Google Scholar
  11. 11.
    Russom, P.: Big data analytics. TDWI Best Practices Report, Fourth Quarter (2011)Google Scholar
  12. 12.
    Li, X., Dong, X.L., Lyons, K., Meng, W., Srivastava, D.: Truth finding on the deep web: is the problem solved? In: Proceedings of the 39th International Conference on Very Large Data Bases, PVLDB 2013, pp. 97–108. VLDB Endowment (2013)Google Scholar
  13. 13.
    Buneman, P., Davidson, S.B.: Data provenance–the foundation of data quality (2013)Google Scholar
  14. 14.
    Emaldi, M., Pena, O., Lázaro, J., Lápez-de-Ipiã, D., Vanhecke, S., Mannens, E.: To trust, or not to trust: Highlighting the need for data provenance in mobile apps for smart cities. In: Proceedings of the 3rd International Workshop on Information Management for Mobile Applications, pp. 68–71 (2013)Google Scholar
  15. 15.
    Hartig, O., Zhao, J.: Using web data provenance for quality assessment. In: Proceedings of the International Workshop on Semantic Web and Provenance Management, Washington DC, USA (2009)Google Scholar
  16. 16.
    Bizer, C., Cyganiak, R.: Quality-driven information filtering using the WIQA policy framework. Web Semantics: Science, Services and Agents on the World Wide Web 7(1), 1–10 (2009)CrossRefGoogle Scholar
  17. 17.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: Dbpedia: A nucleus for a web of open data. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)Google Scholar
  18. 18.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia-a crystallization point for the web of data. Web Semantics: Science, Services and Agents on the World Wide Web 7(3), 154–165 (2009)CrossRefGoogle Scholar
  19. 19.
    Tummarello, G., Delbru, R., Oren, E.: Weaving the open linked data. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 552–565. Springer, Heidelberg (2007)Google Scholar
  20. 20.
    Tummarello, G., Cyganiak, R., Catasta, M., Danielczyk, S., Delbru, R., Decker, S.: Sig. ma: Live views on the web of data. Web Semantics: Science, Services and Agents on the World Wide Web 8(4), 355–364 (2010)CrossRefGoogle Scholar
  21. 21.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Communications Magazine 40(8), 102–114 (2002)CrossRefGoogle Scholar
  22. 22.
    Sanchez, L., Galache, J.A., Gutierrez, V., Hernandez, J., Bernat, J., Gluhak, A., Garcia, T.: SmartSantander: the meeting point between future internet research and experimentation and the smart cities. In: Future Network & Mobile Summit (FutureNetw 2011), pp. 1–8 (2011)Google Scholar
  23. 23.
    Le-Phuoc, D., Quoc, H.N.M., Parreira, J.X., Hauswirth, M.: The linked sensor middleware–connecting the real world and the semantic web. In: Proceedings of the Semantic Web Challenge (2011)Google Scholar
  24. 24.
    O’reilly, T.: What is web 2.0: Design patterns and business models for the next generation of software. Communications & Strategies (1), 17 (2007)Google Scholar
  25. 25.
    Maynard, D., Tablan, V., Ursu, C., Cunningham, H., Wilks, Y.: Named entity recognition from diverse text types. In: Recent Advances in Natural Language Processing 2001 Conference, pp. 257–274 (2001)Google Scholar
  26. 26.
    Sixto, J., Pena, O., Klein, B., López-de-Ipiña, D.: Enable tweet-geolocation and don’t drive ERTs crazy! improving situational awareness using twitter. In: SMERST 2013: Social Media and Semantic Technologies in Emergency Response, Coventry, UK, vol. 1, pp. 27–31 (2013)Google Scholar
  27. 27.
    Martins, B., Anastácio, I., Calado, P.: A machine learning approach for resolving place references in text. In: Geospatial Thinking, pp. 221–236. Springer (2010)Google Scholar
  28. 28.
    Abel, F., Hauff, C., Houben, G.J., Stronkman, R., Tao, K.: Twitcident: fighting fire with information from social web streams. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 305–308 (2012)Google Scholar
  29. 29.
    Vieweg, S., Hughes, A.L., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1079–1088 (2010)Google Scholar
  30. 30.
    Hughes, A.L., Palen, L.: Twitter adoption and use in mass convergence and emergency events. International Journal of Emergency Management 6(3), 248–260 (2009)CrossRefGoogle Scholar
  31. 31.
    Celino, I., Cerizza, D., Contessa, S., Corubolo, M., Dell’Aglio, D., Valle, E.D., Fumeo, S.: Urbanopoly – a social and location-based game with a purpose to crowdsource your urban data. In: Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust, SOCIALCOM-PASSAT 2012, pp. 910–913. IEEE Computer Society, Washington, DC (2012)Google Scholar
  32. 32.
    Celino, I., Contessa, S., Corubolo, M., Dell’Aglio, D., Valle, E.D., Fumeo, S., Krüger, T.: UrbanMatch - linking and improving smart cities data. In: Bizer, C., Heath, T., Berners-Lee, T., Hausenblas, M. (eds.) Linked Data on the Web. CEUR Workshop Proceedings, CEUR-WS, vol. 937 (2012)Google Scholar
  33. 33.
    Braun, M., Scherp, A., Staab, S.: Collaborative semantic points of interests. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part II. LNCS, vol. 6089, pp. 365–369. Springer, Heidelberg (2010)Google Scholar
  34. 34.
    Noy, N.F., McGuinness, D.L.: Ontology development 101: A guide to creating your first ontology. Stanford knowledge systems laboratory technical report KSL-01-05 and Stanford medical informatics technical report SMI-2001-0880 (2001)Google Scholar
  35. 35.
    Emaldi, M., Lázaro, J., Aguilera, U., Peña, O., López-de-Ipiña, D.: Short paper: Semantic annotations for sensor open data. In: Proceedings of the 5th International Workshop on Semantic Sensor Networks, SSN 2012, pp. 115–120 (2012)Google Scholar
  36. 36.
    Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K.: Semantic sensor network XG final report. W3C Incubator Group Report (2011)Google Scholar
  37. 37.
    Raskin, R.G., Pan, M.J.: Knowledge representation in the semantic web for earth and environmental terminology (SWEET). Computers & Geosciences 31(9), 1119–1125 (2005)CrossRefGoogle Scholar
  38. 38.
    d’Aquin, M., Nikolov, A., Motta, E.: Enabling lightweight semantic sensor networks on android devices. In: The 4th International Workshop on Semantic Sensor Networks (SSN 2011) (October/Autumn 2011)Google Scholar
  39. 39.
    Emaldi, M., Lázaro, J., Laiseca, X., López-de-Ipiña, D.: LinkedQR: improving tourism experience through linked data and QR codes. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds.) UCAmI 2012. LNCS, vol. 7656, pp. 371–378. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  40. 40.
    Raimond, Y., Abdallah, S., Sandler, M., Giasson, F.: The music ontology. In: ISMIR 2007: 8th International Conference on Music Information Retrieval, Vienna, Austria, pp. 417–422 (September 2007)Google Scholar
  41. 41.
    Weibel, S., Kunze, J., Lagoze, C., Wolf, M.: Dublin core metadata for resource discovery. Internet Engineering Task Force RFC 2413, 222 (1998)Google Scholar
  42. 42.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)Google Scholar
  43. 43.
    Stasch, C., Schade, S., Llaves, A., Janowicz, K., Bröring145, A.: Aggregating linked sensor data. Semantic Sensor Networks, 46 (2011)Google Scholar
  44. 44.
    Ayers, A., Völkel, M.: Cool uris for the semantic web. Woking Draft. W3C (2008)Google Scholar
  45. 45.
    Bizer, C., Schultz, A.: The berlin sparql benchmark. International Journal on Semantic Web and Information Systems (IJSWIS) 5(2), 1–24 (2009)CrossRefGoogle Scholar
  46. 46.
    Bizer, C., Cyganiak, R.: D2r server-publishing relational databases on the semantic web. In: Proceedings of the 5th International Semantic Web Conference, p. 26 (2006)Google Scholar
  47. 47.
    Gil, Y., Artz, D.: Towards content trust of web resources. Web Semantics: Science, Services and Agents on the World Wide Web 5(4), 227–239 (2007)CrossRefGoogle Scholar
  48. 48.
    Hartig, O.: Provenance information in the web of data. In: Proceedings of the WWW 2009 Workshop on Linked Data on the Web, LDOW 2009 (2009)Google Scholar
  49. 49.
    Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwasnikowska, N., Miles, S., Missier, P., Myers, J., Plale, B., Simmhan, Y., Stephan, E., Van den Bussche, J.: The open provenance model core specification (v1.1). Future Generation Computer Systems 27(6), 743–756 (2011)CrossRefGoogle Scholar
  50. 50.
    Belhajjame, K., B’Far, R., Cheney, J., Coppens, S., Cresswell, S., Gil, Y., Groth, P., Klyne, G., Lebo, T., McCusker, J., Miles, S., Myers, J., Sahoo, S., Tilmes, C.: PROV-DM: The PROV data model (2013)Google Scholar
  51. 51.
    De Nies, T., Coppens, S., Mannens, E., Van de Walle, R.: Modeling uncertain provenance and provenance of uncertainty in W3C PROV. In: Proceedings of the 22nd International Conference on World Wide Web Companion, Rio de Janeiro, Brazil, pp. 167–168 (2013)Google Scholar
  52. 52.
    Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Silk-a link discovery framework for the web of data. In: Proceedings of the International Semantic Web Conference 2010 Posters & Demonstrations Track. Citeseer (2009)Google Scholar
  53. 53.
    Ngomo, A.C.N., Auer, S.: Limes: a time-efficient approach for large-scale link discovery on the web of data. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, pp. 2312–2317. AAAI Press (2011)Google Scholar
  54. 54.
    Sequeda, J., Corcho, O., Taylor, K., Ayyagari, A., Roure, D.D.: Linked stream data: A position paper. In: Proceedings of the 2nd International Workshop on Semantic Sensor Networks (SSN 2009) at ISWC 2009. CEUR Workshop Proceedings, vol. 522, pp. 148–157 (November 2009)Google Scholar
  55. 55.
    Della Valle, E., Ceri, S., van Harmelen, F., Fensel, D.: It’s a streaming world! reasoning upon rapidly changing information. IEEE Intelligent Systems 24(6), 83–89 (2009)CrossRefGoogle Scholar
  56. 56.
    Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  57. 57.
    Khan, M., Khan, S.S.: Data and information visualization methods, and interactive mechanisms: A survey. International Journal of Computer Applications 34(1), 1–14 (2011)CrossRefGoogle Scholar
  58. 58.
    Brunetti, J.M., Auer, S., García, R.: The linked data visualization model. In: International Semantic Web Conference (Posters & Demos) (2012)Google Scholar
  59. 59.
    Cyganiak, Richard and Reynold, Dave. The RDF Data Cube Vocabulary (2013),

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mikel Emaldi
    • 1
    Email author
  • Oscar Peña
    • 1
  • Jon Lázaro
    • 1
  • Diego López-de-Ipiña
    • 1
  1. 1.Deusto Institute of TechnologyDeustoTech, University of DeustoBilbaoSpain

Personalised recommendations