access icon free Follow a guide to solve urban problems: the creation and application of urban knowledge graph

It is a hot research topic today to find out the potential knowledge from the scattered urban data and take advantage of the relationship between the knowledge to solve the challenges of urban governance and smart city construction. The urban knowledge graph is an effective way to establish the relationship between the knowledge and address the urban issues. This study proposes the methodology to create an urban knowledge graph and its framework. It elaborates the approaches of urban knowledge acquisition, reasoning and expression. Furthermore, a hybrid reasoning algorithm known as EG is given based on expectation-maximisation algorithm and Gibbs algorithm, which has the complementary advantages of the both methods. Through a study case, this study illustrates constructing and working process of an urban knowledge graph. The case shows that the urban knowledge graph has a good application prospect.

Inspec keywords: inference mechanisms; knowledge acquisition; expectation-maximisation algorithm; graph theory

Other keywords: hybrid reasoning algorithm; Gibbs algorithm; urban knowledge graph; expectation-maximisation algorithm; EG; urban knowledge acquisition; urban problems

Subjects: Other topics in statistics; Combinatorial mathematics; Knowledge engineering techniques

References

    1. 1)
      • 18. Gan, G.: ‘Data clustering: theory, algorithms, and applications’, SIAM: Society for Industrial and Applied Mathematics, 2007.
    2. 2)
      • 25. Yam, K.-Y., Siu, W.-C., Law, N.-F.: ‘Effective bi-directional people flow counting for real time surveillance system’. Int. Conf. on Consumer Electronics (ICCE), IEEE Press, January 2011, pp. 863864.
    3. 3)
      • 26. Matsuno, Y., Ito, M., Sezaki, K.: ‘Impact of time-varying population density on location privacy preservation level’. Int. Workshop on the Impact of Human Mobility in Pervasive Systems and Applications (PerMoby), IEEE Press, March 2016, pp. 16.
    4. 4)
      • 23. Hoang, M.X., Zheng, Y., Singh, A.K.: ‘FCCF: forecasting citywide crowd flows based on big data’. Proceeding GIS 2016 Proc. of the 24th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems Article No.6., October 2016, pp. 110.
    5. 5)
      • 19. Koller, D.: ‘Probabilistic graphical models: principles and techniques’ (The MIT Press, 2009).
    6. 6)
      • 9. Yoon, H.-G., Song, H.-J., Park, S.-B., et al: ‘A translation-based knowledge graph embedding preserving logical property of relations’. Proc. of NAACL-HLT, California, US., June 2016, pp. 907916.
    7. 7)
      • 12. Pujara, J.: ‘Probabilistic models for scalable knowledge graph construction’. PhD thesis, University of Maryland, 2016.
    8. 8)
      • 7. Xie, R., Liu, Z., Jia, J., et al: ‘Representation learning of knowledge graphs with entity descriptions’. Thirtieth AAAI Conf. on Artificial Intelligence, Arizona, US., February 2016.
    9. 9)
      • 24. Kishore, P.V.V., Rahul, R., Sravya, K., et al: ‘Crowd density analysis and tracking’, Advances in Computing, Communications and Informatics (ICACCI), IEEE Press, August2015, pp. 12091213.
    10. 10)
      • 17. Zhou, Q., Leydesdorff, L.: ‘The normalization of occurrence and co-occurrence matrices in bibliometrics using cosine similarities and ochiai coefficients’, View issue TOC, 2016, 67, (11), pp. 28052814.
    11. 11)
      • 15. Le-Phuoc, D., Quoc, H.N.M., Quoc, H.N., et al: ‘The graph of things: A step towards the live knowledge graph of connected things’, Web Semantics: Sci., Serv. Agents World Wide Web, 2016, 37, (38), pp. 2535.
    12. 12)
      • 11. Sathish, S., Patankar, A., Neema, N., et al: ‘Evolving the user graph: from unsupervised topic models to knowledge assisted networks’. IEEE 9th Int. Conf. on Semantic Computing, California, USA, February 2015, pp. 136141.
    13. 13)
      • 3. Lopez, V., Miñana, G., Sánchez, O., et al: ‘Big + open data: Some applications for a Smart city’. IEEE Int. Conf. on Progress in Informatics and Computing, Nanjing, China, December 2015, pp. 384389.
    14. 14)
      • 13. McCusker, J.P., Dumontier, M., Yan, R., et al: ‘Finding melanoma drugs through a probabilistic knowledge graph’, PeerJ Preprints, 2016.
    15. 15)
      • 2. Kitchin, R.: ‘The data revolution: big data, open data, data infrastructures & their consequences’, Reg. Stud., 2015, 37, (7), pp. 12.
    16. 16)
      • 10. Szekely, P., Knoblock, C.A., Slepicka1, J., et al: ‘Building and using a knowledge graph to combat human trafficking’. The 14th Int. Semantic Web Conf., New York, USA, October, 2015, pp. 205221.
    17. 17)
      • 21. Darling, W.M.: ‘A theoretical and practical implementation tutorial on topic modeling and gibbs sampling’. Proc. of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Oregon, 2011, pp. 642647.
    18. 18)
      • 16. Liu, Y., Goncalves, J., Ferreira, D., et al: ‘CHI 1994-2013: mapping two decades of intellectual progress through co-word analysis’. CHI 2014 Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, Toronto Canada, April 2014, pp. 35533562.
    19. 19)
      • 5. introducing the Knowledge Graph: things, not strings’, available at https://googleblog.blogspot.jp/2012/05/introducing-knowledge-graph-things-not.html, accessed 15 July2016.
    20. 20)
      • 4. Meijer, A., Bolívar, M.P.R.: ‘Governing the smart city: a review of the literature on smart urban governance’, Int. Rev. Adm. Sci., 2016, 82, (2), pp. 392408.
    21. 21)
      • 22. Zheng, Y., Capra, L., Wolfson, O., et al: ‘Urban computing: concepts, methodologies, and applications’, ACM Trans. Intell. Syst. Technol. (TIST)-Spec. Sect. Urban Comput., 2014, 5, (3), pp. 3855, Article No.38.
    22. 22)
      • 20. McLachlan, G., Krishnan, T.: ‘The EM algorithm and extensions’ (Wiley-Interscience press, 2008, 2nd edn.).
    23. 23)
      • 1. Whitmore, A., Agarwal, A., Xu, L.D.: ‘The internet of things—A survey of topics and trends’, Inf. Syst. Front., 2015, 17, (2), pp. 261274.
    24. 24)
      • 14. Stoffel, F., Fischer, F.: ‘Using a knowledge graph data structure to analyze text documents’. IEEE Symposium on Visual Analytics Science and Technology, Paris, France, November 2014, pp. 331332.
    25. 25)
      • 8. Palomares, T., Ahres, Y., Kangaspunta, J., et al: ‘Wikipedia knowledge graph with deepDive’. Tenth Int. AAAI Conf. on Web and Social Media, Oxford, UK., May 2016, pp. 6571.
    26. 26)
      • 6. De Nies, T., Beecks, C., Godin, F., et al: ‘A distance-based approach for semantic dissimilarity in knowledge graphs’. IEEE Tenth Int. Conf. on Semantic Computing, California, US, February 2016, pp. 254257.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-sen.2016.0189
Loading

Related content

content/journals/10.1049/iet-sen.2016.0189
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading