Skip to main content

Context-Aware Knowledge Fusion for Decision Support

  • Chapter
  • First Online:
Context-Enhanced Information Fusion

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The detailed description of the framework underlying the CADSS can be found in [50, 51].

References

  1. D. Hall, J. Llinas, Handbook of Multisensor Data Fusion (CRC Press, Boca Raton, 2001)

    Google Scholar 

  2. E. Blasch, É. Bossé, D.A. Lambert (eds.), High-level information fusion management and systems design (Artech House, Boston, 2012)

    Google Scholar 

  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. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5711868. Accessed 25 Apr 2015

  4. M.A. Abidi, R.C. Gonzalez, Data Fusion in Robotics and Machine Intelligence (Academic Press, San Diego, 1992)

    MATH  Google Scholar 

  5. A. Appriou, A. Ayoun, S. Benferhat et al., Fusion: general concepts and characteristics. Int. J. Intell. Syst. 16, 1107–1134 (2001)

    Article  MATH  Google Scholar 

  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. 1029–1036

    Google Scholar 

  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. 171–182

    Google Scholar 

  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. 2004–2011

    Google Scholar 

  9. B.V. Dasarathy, Information fusion—what, where, why, when, and how? Inf. Fusion 2(2), 75–76 (2001)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  11. E.L. Waltz, J. Llinas, Multisensor Data Fusion (Artech House, Norwood, MA, 1990)

    Google Scholar 

  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. 66–86

    Google Scholar 

  13. V. Phan-Luong, A framework for integrating information sources under lattice structure. Inf. Fusion 9(2), 278–292 (2008)

    Article  Google Scholar 

  14. A. Preece et al., Kraft: an agent architecture for knowledge fusion. Int. J. Coop. Inf. Syst. 10(1–2), 171–195 (2001)

    Article  Google Scholar 

  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. A. Hunter, R. Summerton, Fusion rule technology (2002–2005). http://www0.cs.ucl.ac.uk/staff/a.hunter/frt/. Accessed 20 Apr 2015

  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. C. Zins, Conceptual approaches for defining data, information, and knowledge. J. Am. Soc. Inf. Sci. Technol. 58(4), 479–493 (2007)

    Google Scholar 

  19. N. Baumgartner et al., BeAware!—Situation awareness, the ontology-driven way. Data Knowl. Eng. 69, 1181–1193 (2010)

    Article  Google Scholar 

  20. J. Garcia, et al., Context-based multi-level information fusion for harbor surveillance. Inf. Fusion (2014). http://dx.doi.org/10.1016/j.inffus.2014.01.011

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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. 291–298

    Google Scholar 

  24. O. Etzioni, D. Weld, A softbot-based interface to the internet. Commun. ACM 37(7), 72–76 (1994)

    Article  Google Scholar 

  25. A. Levy, The information manifold approach to data integration. IEEE Intell. Syst. 13(5), 12–16 (1998)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  MATH  Google Scholar 

  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. 192–201

    Google Scholar 

  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. 11–20

    Google Scholar 

  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. 1340–1346

    Google Scholar 

  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. D. Ash, B. Hayes-Roth, Using action-based hierarchies for real-time diagnosis. Artif. Intell. 88, 317–348 (1996)

    Article  MATH  Google Scholar 

  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. 19–40

    Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. http://isif.org/fusion/proceedings/fusion01CD/fusion/searchengine/pdf/ThC23.pdf. Accessed 20 Apr 2015

  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). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5712064. Accessed 20 Apr 2015

  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). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4632375. Accessed 20 Apr 2015

  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. http://www.dodccrp.org/events/6th_ICCRTS/Tracks/Papers/Track6/065_tr6.pdf. Accessed 20 Apr 2015

  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. 1323–1329

    Google Scholar 

  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. 587–592

    Google Scholar 

  42. H.A. Grebla, C.O. Cenan, L. Stanca, Knowledge fusion in academic networks. Broad Res. Artif. Intell. Neurosci. (BRAIN) 1(2) (2010). http://www.edusoft.ro/brain/index.php/brain/article/download/60/145. Accessed 14 Apr 2015

  43. C. Jonquet et al., NCBO resource index: ontology-based search and mining of biomedical resources. J. Web Semant. 9(3), 316–324 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 2979–2988

    Google Scholar 

  47. H.A. Simon, Making management decisions: the role of intuition and emotion. Acad. Manag. Exec. 1, 57–64 (1987)

    Article  Google Scholar 

  48. E. Tsang, Foundations of Constraint Satisfaction (Academic Press, London, 1995)

    Google Scholar 

  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. 72–83

    Google Scholar 

  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

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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

    Article  Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nikolay Shilov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland (outside the USA)

About this chapter

Cite this chapter

Smirnov, A., Levashova, T., Shilov, N. (2016). Context-Aware Knowledge Fusion for Decision Support. In: Snidaro, L., García, J., Llinas, J., Blasch, E. (eds) Context-Enhanced Information Fusion. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-28971-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28971-7_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28969-4

  • Online ISBN: 978-3-319-28971-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics