Multimedia Tools and Applications

, Volume 51, Issue 3, pp 1175–1200 | Cite as

Concept modeling: From origins to multimedia

  • Sanida Omerovic
  • Zoran Babovic
  • Zhilbert Tafa
  • Veljko Milutinovic
  • Sašo Tomazic


The origins of concept modeling are in the field of artificial intelligence. This is where the initial algorithms were introduced first. With the emerging developments in the field of multimedia systems, a strong need is generated to examine and implement concepts-based retrieval of multimedia-contents, from large data bases or from the Internet. The early works were based on appropriate modifications of classical approaches. The latest developments utilize the algorithms that make sense only in the case of multimedia systems. This paper presents a number of classical approaches to concept modeling and their applicability to multimedia. Then it discusses a number of approaches introduced specifically for multimedia. Finally it presents an approach which was fully implemented and tested in an academic environment for industry needs.


Concepts Knowledge Ontology Semantics Multimedia Retrieval Understanding Data Relations Representation 



The authors would like to thank Charles Milligan of Sun Microsystems, USA, and Gerald O’Nions of StorageTek, France, who initiated their research interest in this exciting field. Also, to Tom Lincoln of the University of Southern California, USA, Roger Shannon of Duke University, USA, and William Robertson of Dalhousie University, Canada, who provided qualified and detailed feedback on this research. Finally, discussions with Nobel Laureates, Martin Perl of Stanford University and Jerome Friedman of MIT, helped shape up the final version of this paper.


  1. 1.
    Babovic Z et al (2010) The media retrieval tool, Proceedings of the VIPSI 2010 AMALFI Conference, Amalfi, Italy, March 2010Google Scholar
  2. 2.
    Ballan L, Bertini M, Bimbo AD, Serra G (2010) Video annotation and retrieval using ontologies and rule learning. IEEE MultiMedia, Unconditionally accepted, pending publicationGoogle Scholar
  3. 3.
    Bryant RE (2007) Data-intensive supercomputing: the case for DISC, Technical report, School of Computer Science, Carnegie Mellon UniversityGoogle Scholar
  4. 4.
    Chan C (2004) The knowledge modelling system and its application, Canadian Conference on Electrical and Computer Engineering, 2–5 May 2004, pp 1353–1356, Vol. 3Google Scholar
  5. 5.
    Chein M, Mugnies M (1992) Conceptual graphs: fondamental notions. Rev Intell Artif 6(4):365–406CrossRefGoogle Scholar
  6. 6.
    Chen P (2007) The entity-relationship model—toward a unified view of data, ACM Press, ACM Transactions on Database Systems 1(1):9–36Google Scholar
  7. 7.
    Chen H, Finin T (2003) An ontology for context aware pervasive computing environments. Cambridge University Press, September 2003, Vol. 18, Issue 03Google Scholar
  8. 8.
    Chua T, Pung H, Lu G, Jong H (1994) A concept-based image retrieval system, Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences. Vol. III: Information systems: decision support and knowledge-based systems, Wailea, Hawaii, USA, 4–7 Jan. 1994, pp 590–598Google Scholar
  9. 9.
    Cook D (2007) SUBDUE graph based knowledge discovery.
  10. 10.
    Deacon T (1998) The symbolic species – the co-evolution of language and the brain. W.W.Northon & Company, USAGoogle Scholar
  11. 11.
    Djordjevic N, Mestrovic N, Mijovic DJ, Dimovski B (2010) Applied concept modeling techniques for semantic data retrieval. IPSI Transaction on Internet Research 6(1):27–30Google Scholar
  12. 12.
    Diligenti M, Kovacevic M (2010) Visual pagerank: improving the random surfer model using visual features. IPSI Transaction on Internet Research 6(1):18–26Google Scholar
  13. 13.
    Dou D, McDermott D, Qi P (2004) Ontology translation by ontology merging and automated reasoning. Yale University, New HavenGoogle Scholar
  14. 14.
    Frawley W, Piatetsky-Shapiro G, Matheus J (1992) Knowledge discovery in databases: an overview. The American Association for Artificial Intelligence, USAGoogle Scholar
  15. 15.
    Fujihara H, Simmons D (1997) Knowledge conceptualization tool. IEEE Trans Knowl Data Eng Archive 9(2):209–220CrossRefGoogle Scholar
  16. 16.
    Gauch S, Madrid J, Induri S, Ravindran D, Chadalavada S (2002) KeyConcept: a conceptual search engine, Information and Telecommunication Technology Center, Technical Report: ITTC-FY2004-TR-8646–37. University of Kansas, USAGoogle Scholar
  17. 17.
    Giugno R, Sasha D (2007) GraphGrep.
  18. 18.
    Gomez-Perez A, Corcho O (2002) Ontology languages for the semantic web. IEEE Intell Syst 17(1):54–60CrossRefGoogle Scholar
  19. 19.
    Halladay S, Milligan C (2004) The application of network science principles to knowledge simulation, Proceedings of the 37th Annual Hawaii International Conference on System Sciences, Hawaii, 5–8 Jan. 2004Google Scholar
  20. 20.
    Halpin T (2007) Object role modeling, Neumont University, USA,
  21. 21.
    Han J, Huang Y, Cercone N, Fu Y (1996) Intelligent query answering by knowledge discovery techniques. IEEE Trans Knowl Data Eng 8(3):373–390CrossRefGoogle Scholar
  22. 22.
    Hawkins J (2007) Learn like a human, IEEE Spectrum on-line, April 2007Google Scholar
  23. 23.
    Hollink L, Worring M, Schreiber ATH (2005) Building a visual ontology for video retrieval, In Proc. of the ACM Multimedia, pp 479–482, November 2005Google Scholar
  24. 24.
    Hoogs A, Rittscher J, Stein G, Schmiederer J (2003) Video content annotation using visual analysis and a large semantic knowledgebase, In Proc. of the Conf. on Computer Vision and Pattern RecognitionGoogle Scholar
  25. 25.
    Hunter J (2001) Adding multimedia to the semantic web—building an mpeg-7 ontology, In International Semantic Web Working SymposiumGoogle Scholar
  26. 26.
    Jung MY, Park SH (2008) Semantic-based scene retrieval using ontologies for video server, ITC-CSCC-2008, Japan, pp 45–48Google Scholar
  27. 27.
    Milutinovic V et al (2010) Concept modeling for multimedia contents, Proceedings of the VIPSI 2010 AMALFI Conference, Amalfi, Italy, March 2010Google Scholar
  28. 28.
    Nakabasami C (2002) An inductive approach to assertional minning for web ontology revision, International Semantic Web Conference (ISWC2002), Sardinia, Italy, 9–12 June 2002Google Scholar
  29. 29.
    Novak J, Cañas A (2005) The theory of underlying concept maps and how to construct them, Technical ReportFlorida Institute for Human and Machine Cognition CmapTools 2006-01, USAGoogle Scholar
  30. 30.
    OWL (2004) Web Ontology Working Group.
  31. 31.
    Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5(3):239–266Google Scholar
  32. 32.
    Rubin DL, Supekar K, Mongkolwat P, Kleper V, Channin DS (2009) Annotation and image markup: accessing and interoperating with the semantic content in medical imaging. IEEE Intell Syst 24(1):57–65CrossRefGoogle Scholar
  33. 33.
    Salton G, Wong A (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620MATHCrossRefGoogle Scholar
  34. 34.
    Siegel et al (1985) Basics of image understanding, Purdue University, An ONR Technical ReportGoogle Scholar
  35. 35.
    Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12)Google Scholar
  36. 36.
    Sowa J (2000) Ontology, metadata, and semiotics, Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic and Computational Issues table of contents, pp 55–81Google Scholar
  37. 37.
    Sowa J, Tepfenhart W, Cyre W (1999) Conceptual graphs: draft proposed American National Standard. Springer-Verlag, Berlin, Germany, Lecture Notes in Computer Science, pp 1–65Google Scholar
  38. 38.
    The concept modeler software package. Stanford University, 2006Google Scholar
  39. 39.
    The DAML Ontologies (2007) DARPA, USA,
  40. 40.
    The INIS Thesauri (1981) International Atomic Energy Agency (IAEA), Vienna, Austria, January 1981Google Scholar
  41. 41.
    Unified Modeling Language (2007) Object Management Group.
  42. 42.
    Varga E, Furlan B, Milutinovic V (2010) Document filter based on extracted concepts. IPSI Transaction on Internet Research 6(1):5–9Google Scholar
  43. 43.
    Viola P, Jones M (2001) Robust real-time object detection. Technical Report CRL 20001/01, Cambridge Research LaboratoryGoogle Scholar
  44. 44.
    Voss A, Nakata K, Juhnke M (1999) Concepts as knowledge handles in collaborative document management, International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, Stanford, USA, 16–18 June 1999, pp 245–252Google Scholar
  45. 45.
    Vrochidis S, Dulaverakis C, Gounaris A, Nidelkou E, Makris L, Kompatsiaris Y (2008) A hybrid ontology and visual-based retrieval model for cultural heritage multimedia collections. Int J Metadata Semant Ontol 3(3):167–182CrossRefGoogle Scholar
  46. 46.
    Wan X (2009) Combining content and context similarities for image retrieval, Book: Advances in Information Retrieval, Springer Berlin / Heidelberg, pp 749–754Google Scholar
  47. 47.
    Woods W (1997) Conceptual indexing: a better way to organize knowledge, Sun Microsystems, USA, Technical Report: TR-97-61Google Scholar
  48. 48.
    Yan R, Fleury MO, Merler M, Natsev A, Smith JR (2009) Large-scale multimedia semantic concept modeling using robust subspace bagging and MapReduce, Proceedings of the First ACM Workshop on Large-scale Multimedia Retrieval and Mining. ACM, New York, pp 35–42Google Scholar
  49. 49.
    Zellweger P (2003) A knowledge–based model to database retrieval, Proceedings of the International Conference on Integration of Knowledge Intensive Multi-Agent Systems, 30 Sept.–4 Oct. 2003, pp 747–753Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Sanida Omerovic
    • 1
  • Zoran Babovic
    • 2
  • Zhilbert Tafa
    • 3
  • Veljko Milutinovic
    • 2
    • 4
  • Sašo Tomazic
    • 1
  1. 1.University of LjubljanaLjubljanaSlovenia
  2. 2.University of BelgradeBelgradeSerbia
  3. 3.University of Podgorica and Telekom MontenegroPodgoricaMontenegro
  4. 4.Singidunum UniversityBelgradeSerbia

Personalised recommendations