Advertisement

A Human-Inspired Model to Represent Uncertain Knowledge in the Semantic Web

  • Salvatore Flavio Pileggi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10862)

Abstract

One of the most evident and well-known limitations of the Semantic Web technology is its lack of capability to deal with uncertain knowledge. As uncertainty is often part of the knowledge itself or can be inducted by external factors, such a limitation may be a serious barrier for some practical applications. A number of approaches have been proposed to extend the capabilities in terms of uncertainty representation; some of them are just theoretical or not compatible with the current semantic technology; others focus exclusively on data spaces in which uncertainty is or can be quantified. Human-inspired models have been adopted in the context of different disciplines and domains (e.g. robotics and human-machine interaction) and could be a novel, still largely unexplored, pathway to represent uncertain knowledge in the Semantic Web. Human-inspired models are expected to address uncertainties in a way similar to the human one. Within this paper, we (i) briefly point out the limitations of the Semantic Web technology in terms of uncertainty representation, (ii) discuss the potentialities of human-inspired solutions to represent uncertain knowledge in the Semantic Web, (iii) present a human-inspired model and (iv) a reference architecture for implementations in the context of the legacy technology.

References

  1. 1.
    Berners-Lee, T., Hendler, J., Lassila, O., et al.: The semantic web. Sci. Am. 284(5), 28–37 (2001)CrossRefGoogle Scholar
  2. 2.
    Hayes, P., McBride, B.: RDF semantics. W3C recommendation 10 (2004)Google Scholar
  3. 3.
    McGuinness, D.L., Van Harmelen, F., et al.: Owl web ontology language overview. In: W3C recommendation 10(10) (2004)Google Scholar
  4. 4.
    Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M., et al.: SWRL: a semantic web rule language combining OWL and RuleML. W3C Member Submission 21, 79 (2004)Google Scholar
  5. 5.
    Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: implementing the semantic web recommendations. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters, pp. 74–83. ACM (2004)Google Scholar
  6. 6.
    Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. Web Semant. Sci. Serv. Agents World Wide Web 5(2), 51–53 (2007)CrossRefGoogle Scholar
  7. 7.
    Shearer, R., Motik, B., Horrocks, I.: HermiT: a highly-efficient OWL reasoner. In: OWLED, vol. 432, p. 91 (2008)Google Scholar
  8. 8.
    ARQ - A SPARQL processor for Jena. http://jena.apache.org/documentation/query/index.html. Accessed 15 Feb 2018
  9. 9.
    Gennari, J.H., Musen, M.A., Fergerson, R.W., Grosso, W.E., Crubézy, M., Eriksson, H., Noy, N.F., Tu, S.W.: The evolution of protégé: an environment for knowledge-based systems development. Int. J. Hum Comput Stud. 58(1), 89–123 (2003)CrossRefGoogle Scholar
  10. 10.
    Murugesan, S.: Understanding web 2.0. IT professional 9(4), 34–41 (2007)CrossRefGoogle Scholar
  11. 11.
    Guha, R.V., Brickley, D., Macbeth, S.: Schema.org: evolution of structured data on the web. Commun. ACM 59(2), 44–51 (2016)CrossRefGoogle Scholar
  12. 12.
    Straccia, U.: A fuzzy description logic for the semantic web. Capturing Intell. 1, 73–90 (2006)CrossRefGoogle Scholar
  13. 13.
    Kana, D., Armand, F., Akinkunmi, B.O.: Modeling uncertainty in ontologies using rough set. Int. J. Intell. Syst. Appl. 8(4), 49–59 (2016)Google Scholar
  14. 14.
    da Costa, Paulo Cesar G., Laskey, Kathryn B., Laskey, Kenneth J.: PR-OWL: a Bayesian ontology language for the semantic web. In: da Costa, Paulo Cesar G., d’Amato, Claudia, Fanizzi, Nicola, Laskey, Kathryn B., Laskey, Kenneth J., Lukasiewicz, Thomas, Nickles, Matthias, Pool, Michael (eds.) URSW 2005-2007. LNCS (LNAI), vol. 5327, pp. 88–107. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-89765-1_6CrossRefGoogle Scholar
  15. 15.
    Pileggi, S.F.: Probabilistic semantics. Procedia Comput. Sci. 80, 1834–1845 (2016)CrossRefGoogle Scholar
  16. 16.
    Udrea, O., Subrahmanian, V., Majkic, Z.: Probabilistic RDF. In: 2006 IEEE International Conference on Information Reuse & Integration, pp. 172–177. IEEE (2006)Google Scholar
  17. 17.
    Ding, Z., Peng, Y.: A probabilistic extension to ontology language owl. In: Proceedings of the 37th Annual Hawaii international conference on System Sciences, 2004, p. 10. IEEE (2004)Google Scholar
  18. 18.
    Bechhofer, S.: OWL: web ontology language. In: Liu, L., özsu, M. (eds.) Encyclopedia of Database Systems. Springer, New York (2016).  https://doi.org/10.1007/978-1-4899-7993-3Google Scholar
  19. 19.
    Pan, Jeff Z., Stamou, Giorgos, Tzouvaras, Vassilis, Horrocks, Ian: f-SWRL: a fuzzy extension of SWRL. In: Duch, Włodzisław, Kacprzyk, Janusz, Oja, Erkki, Zadrożny, Sławomir (eds.) ICANN 2005. LNCS, vol. 3697, pp. 829–834. Springer, Heidelberg (2005).  https://doi.org/10.1007/11550907_131CrossRefGoogle Scholar
  20. 20.
    Coradeschi, S., Ishiguro, H., Asada, M., Shapiro, S.C., Thielscher, M., Breazeal, C., Mataric, M.J., Ishida, H.: Human-inspired robots. IEEE Intell. Syst. 21(4), 74–85 (2006)CrossRefGoogle Scholar
  21. 21.
    Moore, R.: PRESENCE: a human-inspired architecture for speech-based human-machine interaction. IEEE Trans. Comput. 56(9), 1176–1188 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Gavrilets, V., Mettler, B., Feron, E.: Human-inspired control logic for automated maneuvering of miniature helicopter. J. Guid. Control Dyn. 27(5), 752–759 (2004)CrossRefGoogle Scholar
  23. 23.
    Yao, Y.: Human-inspired granular computing. Novel developments in granular computing: applications for advanced human reasoning and soft computation, pp. 1–15 (2010)Google Scholar
  24. 24.
    Liang, Q.: Situation understanding based on heterogeneous sensor networks and human-inspired favor weak fuzzy logic system. IEEE Syst. J. 5(2), 156–163 (2011)CrossRefGoogle Scholar
  25. 25.
    Velloso, Pedro B., Laufer, Rafael P., Duarte, Otto C.M.B., Pujolle, Guy: HIT: a human-inspired trust model. In: Pujolle, Guy (ed.) MWCN 2006. ITIFIP, vol. 211, pp. 35–46. Springer, Boston, MA (2006).  https://doi.org/10.1007/978-0-387-34736-3_2CrossRefGoogle Scholar
  26. 26.
    de Jong, S., Tuyls, K.: Human-inspired computational fairness. Auton. Agent. Multi-Agent Syst. 22(1), 103–126 (2011)CrossRefGoogle Scholar
  27. 27.
    Lughofer, E.: Human-inspired evolving machines-the next generation of evolving intelligent systems. IEEE SMC Newslett. 36 (2011)Google Scholar
  28. 28.
    Pesquita, C., Faria, D., Falcao, A.O., Lord, P., Couto, F.M.: Semantic similarity in biomedical ontologies. PLoS Comput. Biol. 5(7), e1000443 (2009)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Pileggi, S.F.: Web of similarity. J. Comput. Sci. (2016)Google Scholar
  30. 30.
    Quilitz, Bastian, Leser, Ulf: Querying distributed RDF data sources with SPARQL. In: Bechhofer, Sean, Hauswirth, Manfred, Hoffmann, Jörg, Koubarakis, Manolis (eds.) ESWC 2008. LNCS, vol. 5021, pp. 524–538. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-68234-9_39CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Systems, Management and LeadershipUniversity of Technology SydneyUltimoAustralia

Personalised recommendations