Semantic and Reasoning Systems for Cities and Citizens

  • Spyros Kotoulas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8714)


The Semantic Web is finally leaving the lab. In this article, we examine some practical, industry-oriented Semantic Web systems and discuss the costs and benefits on this disruptive technology. We focus on applications for cities and citizens and present a set of key challenges and solutions made possible using semantics at scale. When applicable, we report on the differentiating factors for Semantic Technologies, showcasing their unique capabilities, as well as the cost of this paradigm shift.


Link Data Medical Expenditure Panel Survey Reasoning System Triple Pattern Link Open 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.


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  1. 1.
    Davenport, T.H., Harris, J.G.: Competing on analytics: the new science of winning. Harvard Business Press (2013)Google Scholar
  2. 2.
    Marmot, M., Wilkinson, R.: Social determinants of health. Oxford University Press (2005)Google Scholar
  3. 3.
    García-Silva, A., Gómez-Pérez, A., Suárez-Figueroa, M.C., Villazón-Terrazas, B.: A pattern based approach for re-engineering non-ontological resources into ontologies. In: Domingue, J., Anutariya, C. (eds.) ASWC 2008. LNCS, vol. 5367, pp. 167–181. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Pivk, A.: Automatic ontology generation from web tabular structures. AI Communications 19, 2006 (2005)MathSciNetGoogle Scholar
  5. 5.
    Hurst, M.: Layout and language: Challenges for table understanding on the web, pp. 27–30 (2001)Google Scholar
  6. 6.
    Venetis, P., Halevy, A., Madhavan, J., Paşca, M., Shen, W., Wu, F., Miao, G., Wu, C.: Recovering semantics of tables on the web. VLDB Endow. 4(9), 528–538 (2011)CrossRefGoogle Scholar
  7. 7.
    Alani, H., Dupplaw, D., Sheridan, J., O’Hara, K., Darlington, J., Shadbolt, N.R., Tullo, C.: Unlocking the potential of public sector information with semantic web technology. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 708–721. Springer, Heidelberg (2007)Google Scholar
  8. 8.
    Han, L., Finin, T.W., Parr, C.S., Sachs, J., Joshi, A.: Rdf123: From spreadsheets to rdf. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 451–466. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Scharffe, F., Atemezing, G., Troncy, R., Gandon, F., et al.: Enabling linked-data publication with the datalift platform. In (AAAI 2012) Workshop on Semantic Cities (2012)Google Scholar
  10. 10.
    Maali, F., Cyganiak, R., Peristeras, V.: A publishing pipeline for linked government data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 778–792. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Zhao, J.: Open provenance model vocabulary specification. tech. rep. university of oxford (2010),
  12. 12.
    Kotoulas, S., Lopez, V., Lloyd, R., Sbodio, M.L., Lecue, F., Stephenson, M., Daly, E., Bicer, V., Gkoulalas-Divanis, A., Di Lorenzo, G., et al.: Spud? semantic processing of urban data. Web Semantics: Science, Services and Agents on the World Wide Web (2014)Google Scholar
  13. 13.
    Sandu, D.: Operational and real-time business intelligence. Revista Informatica Economică 3(47), 33–36 (2008)Google Scholar
  14. 14.
    Della Valle, E., Celino, I., Dell’Aglio, D., Grothmann, R., Steinke, F., Tresp, V.: Semantic traffic-aware routing using the larkc platform. IEEE Internet Computing 15(6), 15–23 (2011)CrossRefGoogle Scholar
  15. 15.
    Lécué, F., Tallevi-Diotallevi, S., Hayes, J., Tucker, R., Bicer, V., Sbodio, M.L., Tommasi, P.: Star-city: semantic traffic analytics and reasoning for city. In: IUI, pp. 179–188 (2014)Google Scholar
  16. 16.
    Tallevi-Diotallevi, S., Kotoulas, S., Foschini, L., Lécué, F., Corradi, A.: Real-time urban monitoring in dublin using semantic and stream technologies. In: Alani, H., et al. (eds.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 178–194. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  17. 17.
    Bolles, A., Grawunder, M., Jacobi, J.: Streaming sparql - extending sparql to process data streams. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 448–462. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Rodriguez, A., McGrath, R.E., Liu, Y., Myers, J.D.: Semantic management of streaming data. In: International Workshop on Semantic Sensor Networks at ISWC (2009)Google Scholar
  19. 19.
    Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: Ep-sparql: a unified language for event processing and stream reasoning. In: WWW, pp. 635–644 (2011)Google Scholar
  20. 20.
    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
  21. 21.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: C-sparql: Sparql for continuous querying. In: WWW, pp. 1061–1062 (2009)Google Scholar
  22. 22.
    Ren, Y., Pan, J.Z.: Optimising ontology stream reasoning with truth maintenance system. In: CIKM, pp. 831–836 (2011)Google Scholar
  23. 23.
    Heath, T., Motta, E.: Revyu: Linking reviews and ratings into the web of data. Web Semantics: Science, Services and Agents on the World Wide Web 6(4), 266–273 (2008)CrossRefGoogle Scholar
  24. 24.
    Kotoulas, S., Lopez, V., Sbodio, M.L., Tommasi, P., Stephenson, M., Aonghusa, P.M.: Improving cross-domain information sharing in care coordination using semantic web technologies. In: IUI, pp. 347–352 (2014)Google Scholar
  25. 25.
    Conwell, L.J., Cohen, J.W.: Characteristics of persons with high medical expenditures in the US civilian noninstitutionalized population, 2002. Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality (2005)Google Scholar
  26. 26.
    Marmot, M., Wilkinson, R.: Social determinants of health. Oxford University Press (2009)Google Scholar
  27. 27.
    Kodner, D.L., Spreeuwenberg, C.: Integrated care: meaning, logic, applications, and implications–a discussion paper. International journal of integrated care 2 (2002)Google Scholar
  28. 28.
    Onie, R., Farmer, P., Behforouz, H.: Realigning health with care. Stanford Social Innovation Review 10, 28–35 (2012)Google Scholar
  29. 29.
    Nodine, M., Hee, A., Ngu, H., Bohrer, W.: Semantic brokering over dynamic heterogeneous data sources in infosleuth. In: Proc. of the 15th Inter. Conference on Data Engineering, pp. 358–365. IEEE Computer Society (1999)Google Scholar
  30. 30.
    Kotoulas, S., Lopez, V., Stephenson, M., Tommasi, P., Shen, W., Hu, G., Sbodio, M.L., Bicer, V., Kementsietsidis, A., Rafique, M.M., Ellis, J.B., Erickson, T., Srinivas, K., McAuliffe, K., Xie, G.T., Aonghusa, P.M.: Coordinating social care and healthcare using semantic web technologies. In: International Semantic Web Conference (Posters & Demos), pp. 169–172 (2013)Google Scholar
  31. 31.
    Inmon, W.H.: Building the data warehouse. John Wiley & Sons (2005)Google Scholar
  32. 32.
    Moore, G.: Systems of engagement and the future of enterprise it-a sea change in enterprise it. AIIM, Silver Spring (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Spyros Kotoulas
    • 1
  1. 1.IBM ResearchDublinIreland

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