Context-Aware Knowledge Fusion for Decision Support

  • Alexander Smirnov
  • Tatiana Levashova
  • Nikolay Shilov
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


The purpose of this chapter is to investigate knowledge fusion processes with reference to context-aware decision support. Various knowledge fusion processes and their possible outcomes are analyzed. A context-aware decision support system for emergency management serves as a possible application in which knowledge fusion processes go on. This system provides fused outputs from different knowledge sources. It relies upon context model, which is the key to fuse information/knowledge and to generate useful decisions. The discussion is complemented by examples from a fire response scenario.


Context-aware decision support Constraint-based ontology Information fusion model Knowledge fusion Emergency management Fire response 



The present research was partly supported by the projects funded through grants 14-07-00345, 14-07-00378, 14-07-00427, 15-07-08092 (the Russian Foundation for Basic Research), the Project 213 of the Program 8 “Intelligent information technologies and systems” (the Russian Academy of Sciences (RAS)), the Project 2.2 of the basic research program “Intelligent information technologies, system analysis and automation” (the Nanotechnology and Information Technology Department of the RAS), and grant 074-U01 (the Government of the Russian Federation).


  1. 1.
    D. Hall, J. Llinas, Handbook of Multisensor Data Fusion (CRC Press, Boca Raton, 2001)Google Scholar
  2. 2.
    E. Blasch, É. Bossé, D.A. Lambert (eds.), High-level information fusion management and systems design (Artech House, Boston, 2012)Google Scholar
  3. 3.
    C. Laudy, H. Petersson, K. Sandkuhl, Architecture of knowledge fusion within an integrated mobile security kit, in Proceedings of the 13th International Conference on Information Fusion, Edinburgh, UK, 26–29 July 2010. Accessed 25 Apr 2015
  4. 4.
    M.A. Abidi, R.C. Gonzalez, Data Fusion in Robotics and Machine Intelligence (Academic Press, San Diego, 1992)zbMATHGoogle Scholar
  5. 5.
    A. Appriou, A. Ayoun, S. Benferhat et al., Fusion: general concepts and characteristics. Int. J. Intell. Syst. 16, 1107–1134 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    J.A. Kennewell, B.-N. Vo, An overview of space situational awareness, in Proceedings of the 16th International Conference on Information Fusion, Istanbul, Turkey, 9–12 July 2013, pp. 1029–1036Google Scholar
  7. 7.
    S. Paradis, B.A. Chalmers, R. Carling, P. Bergeron, Towards a generic model for situation and threat assessment, in Digitalization of the Batterfield II. SPIE Aerosense Conference, vol. 3080, Orlando, April 1997, pp. 171–182Google Scholar
  8. 8.
    A.N. Steinberg, C.L. Bowman, Adaptive context discovery and exploitation, in Proceedings of the 16th International Conference on Information Fusion, Istanbul, Turkey, 9–12 July 2013, pp. 2004–2011Google Scholar
  9. 9.
    B.V. Dasarathy, Information fusion—what, where, why, when, and how? Inf. Fusion 2(2), 75–76 (2001)CrossRefGoogle Scholar
  10. 10.
    M.B.A. Haghighat, A. Aghagolzadeh, H. Seyedarabi, Multi-focus image fusion for visual sensor networks in DCT domain. Comput. Electr. Eng. 37(5), 789–797 (2011)CrossRefzbMATHGoogle Scholar
  11. 11.
    E.L. Waltz, J. Llinas, Multisensor Data Fusion (Artech House, Norwood, MA, 1990)Google Scholar
  12. 12.
    C.W. Holsapple, A.B. Whinston, Building blocks for decision support systems, in New Directions for Database Systems, ed. by G. Ariav, J. Clifford (Ablex Publishing Corp, Norwood, 1986), pp. 66–86Google Scholar
  13. 13.
    V. Phan-Luong, A framework for integrating information sources under lattice structure. Inf. Fusion 9(2), 278–292 (2008)CrossRefGoogle Scholar
  14. 14.
    A. Preece et al., Kraft: an agent architecture for knowledge fusion. Int. J. Coop. Inf. Syst. 10(1–2), 171–195 (2001)CrossRefGoogle Scholar
  15. 15.
    R. Scherl, D.L. Ulery, Technologies for army knowledge fusion. Final report ARL-TR-3279 (Monmouth University, Computer Science Department, West Long Branch, Monmouth, 2004)Google Scholar
  16. 16.
    A. Hunter, R. Summerton, Fusion rule technology (2002–2005). Accessed 20 Apr 2015
  17. 17.
    B.C. Landry, B.A. Mathis, N.M. Meara, J.E. Rush, C.E. Young, Definition of some basic terms in computer and information science. J. Am. Soc. Inf. Sci. 24(5), 328–342 (1970)Google Scholar
  18. 18.
    C. Zins, Conceptual approaches for defining data, information, and knowledge. J. Am. Soc. Inf. Sci. Technol. 58(4), 479–493 (2007)Google Scholar
  19. 19.
    N. Baumgartner et al., BeAware!—Situation awareness, the ontology-driven way. Data Knowl. Eng. 69, 1181–1193 (2010)CrossRefGoogle Scholar
  20. 20.
    J. Garcia, et al., Context-based multi-level information fusion for harbor surveillance. Inf. Fusion (2014).
  21. 21.
    J. Gomez-Romero, M.A. Patricio, J. Garcia, J.M. Molina, Ontology-based context representation and reasoning for object tracking and scene interpretation in video. Expert Syst. Appl. 38(6), 7494–7510 (2011). doi: 10.1016/j.eswa.2010.12.118 CrossRefGoogle Scholar
  22. 22.
    M.M. Kokar, C.J. Matheus, K. Baclawski, Ontology-based situation awareness. Inf. Fusion 10(1), 83–98 (2009). doi: 10.1016/j.inffus.2007.01.004 CrossRefGoogle Scholar
  23. 23.
    S. Dumais, M. Banko, E. Brill, J. Lin, A. Ng, Web question answering: is more always better?, in Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, 11–15 Aug 2002, pp. 291–298Google Scholar
  24. 24.
    O. Etzioni, D. Weld, A softbot-based interface to the internet. Commun. ACM 37(7), 72–76 (1994)CrossRefGoogle Scholar
  25. 25.
    A. Levy, The information manifold approach to data integration. IEEE Intell. Syst. 13(5), 12–16 (1998)CrossRefGoogle Scholar
  26. 26.
    X. Nengfu, W. Wensheng, Y. Xiaorong, J. Lihua, Rule-based agricultural knowledge fusion in web information integration. Sensor Lett. 10(8), 635–638 (2012)CrossRefGoogle Scholar
  27. 27.
    A. Preece, K. Hui, A. Gray, P. Marti, T. Bench-Capon, D. Jones, Z. Cui, The KRAFT architecture for knowledge fusion and transformation. Knowl. Based Syst. 13(2–3), 113–120 (1999)Google Scholar
  28. 28.
    M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, S. Slattery, Learning to construct knowledge bases from the World Wide Web. Artif. Intell. 118, 69–113 (2000)CrossRefzbMATHGoogle Scholar
  29. 29.
    J. Gou, J. Yang, Q. Chen, Evolution and evaluation in knowledge fusion system, in IWINAC 2005, International Work-Conference on the Interplay Between Natural and Artificial Computation, ed. by J. Mira, J.R. Alvarez, vol 3562, Las Palmas de Gran Canaria, Canary Islands, Spain, 15–18 June 2005. Lecture Notes in Computer Science (Springer, Heidelberg, 2005), pp. 192–201Google Scholar
  30. 30.
    T.-T. Kuo, S.-S. Tseng, Y.-T. Lin, Ontology-based knowledge fusion framework using graph partitioning, in IEA/AIE 2003, 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, ed. by P.W.H. Chung, C.J. Hinde, M. Ali, vol. 2718, Laughborough, UK, 23–26 June 2003. Lecture Notes in Artificial Intelligence (Springer, Berlin, 2003), pp. 11–20Google Scholar
  31. 31.
    J. Masters, Structured knowledge source integration and its applications to information fusion, in Proceedings of the Fifth International Conference on Information Fusion, vol. 2, Annapolis, Maryland, USA, 8–11 July 2002, pp. 1340–1346Google Scholar
  32. 32.
    S. Amin, C. Byington, M. Watson, Fuzzy inference and fusion for health state diagnosis of hydraulic pumps and motors, in NAFIPS 2005, Annual Meeting of the North American, Detroit, MI, USA, 26–28 June 2005 (Fuzzy Information Processing Society, 2005). doi: 10.1109/NAFIPS.2005.1548499
  33. 33.
    D. Ash, B. Hayes-Roth, Using action-based hierarchies for real-time diagnosis. Artif. Intell. 88, 317–348 (1996)CrossRefzbMATHGoogle Scholar
  34. 34.
    R.N. Carvalho, K.B. Laskey, P.C.G. Costa, M. Ladeira, L.L. Santos, S. Matsumoto, Probabilistic ontology and knowledge fusion for procurement fraud detection in Brazil, in Uncertainty Reasoning for the Semantic Web II, International Workshops URSW 2008–2010 held at ISWC and UniDL 2010 held at Floc, vol. 7123, ed. by F. Bobillo, et al. Lecture Notes in Computer Science (Springer, Heidelberg, 2013), pp. 19–40Google Scholar
  35. 35.
    A. Smirnov, M. Pashkin, T. Levashova, N. Chilov, Fusion-based knowledge logistics for intelligent decision support in network-centric environment. Int. J. Gen. Syst. 34(6), 673–690 (2005)CrossRefzbMATHGoogle Scholar
  36. 36.
    A.C. Boury-Brisset, Towards a knowledge server to support the situation analysis process, in Proceedings of the 4th International Conference on Information Fusion, Montréal, Canada, 7–10 August 2001. Accessed 20 Apr 2015
  37. 37.
    T. Erlandsson, T. Helldin, G. Falkman, L. Niklasson, Information fusion supporting team situation awareness for future fighting aircraft, in Proceedings of the 13th International Conference on Information Fusion, Edinburgh, UK, 26–29 July 2010 (IEEE). Accessed 20 Apr 2015
  38. 38.
    K.B. Laskey, P.C.G. Costa, T. Janssen, Probabilistic ontologies for knowledge fusion, in Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany, 30 June 2008–3 July 2008 (IEEE, 2008). Accessed 20 Apr 2015
  39. 39.
    O.M. Mevassvik, K. Bråthen, B.J. Hansen, A simulation tool to assess recognized maritime picture production in C2 systems, in Proceedings of the 6th International Command and Control Research and Technology Symposium, Annapolis, Maryland, USA, June 2001. Accessed 20 Apr 2015
  40. 40.
    X. Pan, L.N. Teow, K.H. Tan, J.H.B. Ang, G.W. Ng, A cognitive system for adaptive decision making, in Proceedings of the 15th International Conference on Information Fusion, Singapore, 9–12 July 2012, pp. 1323–1329Google Scholar
  41. 41.
    P. Besnard, E. Gregoire, S. Ramon, Logic-based fusion of legal knowledge, in Proceedings of the 15th International Conference on Information Fusion, Singapore, 9–12 July 2012, pp. 587–592Google Scholar
  42. 42.
    H.A. Grebla, C.O. Cenan, L. Stanca, Knowledge fusion in academic networks. Broad Res. Artif. Intell. Neurosci. (BRAIN) 1(2) (2010). Accessed 14 Apr 2015
  43. 43.
    C. Jonquet et al., NCBO resource index: ontology-based search and mining of biomedical resources. J. Web Semant. 9(3), 316–324 (2011)CrossRefGoogle Scholar
  44. 44.
    K.R. Lee, Patterns and processes of contemporary technology fusion: the case of intelligent robots. Asian J. Technol. Innov. 15(2), 45–65 (2007)CrossRefGoogle Scholar
  45. 45.
    L.Y. Lin, Y.J. Lo, Knowledge creation and cooperation between cross-nation R&D institutes. Int. J. Electron. Bus. Manag. 8(1), 9–19 (2010)Google Scholar
  46. 46.
    M.J. Roemer, G.J. Kacprzynski, R.F. Orsagh, Assessment of data and knowledge fusion strategies for prognostics and health management, in 2001 IEEE Aerospace conference, vol. 6, Big Sky, Montana, USA, 10–17 March 2001, pp. 2979–2988Google Scholar
  47. 47.
    H.A. Simon, Making management decisions: the role of intuition and emotion. Acad. Manag. Exec. 1, 57–64 (1987)CrossRefGoogle Scholar
  48. 48.
    E. Tsang, Foundations of Constraint Satisfaction (Academic Press, London, 1995)Google Scholar
  49. 49.
    A. Smirnov, A. Kashevnik, N. Shilov, S. Balandin, I. Oliver, S. Boldyrev, On-the-fly ontology matching in smart spaces: a multi-model approach, in Smart Spaces and Next Generation Wired/Wireless Networking. Proceedings of the Third Conference on Smart Spaces, ruSMART 2010, and the 10th International Conference NEW2AN 2010, vol. 6294, St. Petersburg, Russia, 23–25 Aug 2010. Lecture Notes in Computer Science (Springer, Heidelberg, 2010), pp. 72–83Google Scholar
  50. 50.
    A. Smirnov, T. Levashova, N. Shilov, Patterns for context-based knowledge fusion in decision support. Inf. Fusion 21, 114–129 (2015). doi: 10.1016/j.inffus.2013.10.010 CrossRefGoogle Scholar
  51. 51.
    A. Smirnov, M. Pashkin, N. Chilov, T. Levashova, Constraint-driven methodology for context-based decision support. J. Decis. Syst. 14(3), 279–301 (2005)CrossRefzbMATHGoogle Scholar
  52. 52.
    A. Smirnov, M. Pashkin, N. Chilov, T. Levashova, Agent-based support of mass customization for corporate knowledge management. Eng. Appl. Artif. Intell. 16(4), 349–364 (2003)CrossRefGoogle Scholar
  53. 53.
    A. Smirnov, N. Shilov, T. Levashova, L. Sheremetov, M. Contreras, Ontology-driven intelligent service for configuration support in networked organizations. Knowl. Inf. Syst. 12(2), 229–253 (2007)CrossRefGoogle Scholar
  54. 54.
    A. Smirnov, M. Pashkin, N. Chilov, T. Levashova, F. Haritatos, Knowledge source network configuration approach to knowledge logistics. Int. J. Gen. Syst. 32(3), 251–269 (2003)CrossRefzbMATHGoogle Scholar
  55. 55.
    A. Smirnov, T. Levashova, M. Pashkin, N. Shilov, Semantic interoperability in self-configuring service networks for context-driven decision making. Syst. Inf. Sci. Notes 2(1), 27–32 (2007)Google Scholar
  56. 56.
    A. Smirnov, T. Levashova, N. Shilov, A. Kashevnik, Hybrid technology for self-organization of resources of pervasive environment for operational decision support. Int. J. Artif. Intell. Tools 19(2), 211–229 (2010). doi: 10.1142/S0218213010000121 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland (outside the USA) 2016

Authors and Affiliations

  • Alexander Smirnov
    • 1
    • 2
  • Tatiana Levashova
    • 1
  • Nikolay Shilov
    • 1
  1. 1.St. Petersburg Institute for Informatics and Automation of the Russian Academy of SciencesSt. PetersburgRussian Federation
  2. 2.ITMO UniversitySt. PetersburgRussian Federation

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