ETRI Journal

  • 제42권2호
  • /
  • Pages.247-257
  • /
  • 2020
  • /
  • 1225-6463(pISSN)
  • /
  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
권호 표지
DOI QR코드

DOI QR Code

Spatial experience based route finding using ontologies

  • Barzegar, Maryam (The Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne) ;
  • Sadeghi-Niaraki, Abolghasem (Geoinformation Technology Center of Excellence, Faculty of Geodesy & Geomatics Engineering, K.N.Toosi University of Technology) ;
  • Shakeri, Maryam (Geoinformation Technology Center of Excellence, Faculty of Geodesy & Geomatics Engineering, K.N.Toosi University of Technology)
  • 투고 : 2018.01.11
  • 심사 : 2018.10.05
  • 발행 : 2020.04.03
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Spatial experiences in route finding, such as the ability of finding low-traffic routes, exert a significant influence on travel time in big cities; therefore, the spatial experiences of seasoned individuals such as taxi drivers in route finding can be useful for improving route-finding algorithms and preventing using routes having considerable traffic. In this regard, a spatial experience-based route-finding algorithm is introduced through ontology in this paper. To this end, different methods of modeling experiences are investigated. Then, a modeling method is chosen for modeling the experiences of drivers for route finding depending on the advantages of ontology, and an ontology based on the taxi drivers' experiences is proposed. This ontology is employed to create an ontology-based route-finding algorithm. The results are compared with those of Google maps in terms of route length and travel time at peak traffic time. According to the results, although the route lengths of route-finding method based on the ontology of drivers' experiences in three cases (from nine cases) are greater than that based on Google maps, the travel times are shorter in most cases, and in some routes, the difference in travel time reaches only 10 minutes.

키워드

참고문헌

  1. S. Volo, Conceptualizing experience: a tourist based approach, J. Hospitality Marketing Manag. 18 (2009), 111-126. https://doi.org/10.1080/19368620802590134
  2. R. A. Carlson, Experienced Cognition, Psychology Press, Mahwah, NJ, USA, 1997.
  3. M. Kang and U. Gretzel, Perceptions of museum podcast tours: effects of consumer innovativeness, internet familiarity and podcasting affinity on performance expectancies, Tour. Manag. Perspect. 4 (2012), 155-163. https://doi.org/10.1016/j.tmp.2012.08.007
  4. B. K. Foguem et al., Knowledge formalization in experience feedback processes: an ontology-based approach, Comp. Ind. 59 (2008), 694-710. https://doi.org/10.1016/j.compind.2007.12.014
  5. B. Kamsu‐Foguem and F. H. Abanda, Experience modeling with graphs encoded knowledge for construction industry, Comput. Ind. 70 (2015), 79-88. https://doi.org/10.1016/j.compind.2015.02.004
  6. K. Efthymiou et al., On knowledge reuse for manufacturing systems design and planning: a semantic technology approach, CIRP J. Manuf. Sci. Technol. 8 (2015), 1-11. https://doi.org/10.1016/j.cirpj.2014.10.006
  7. C. Park et al., Knowledge-based AOP framework for business rule aspects in business process, ETRI J. 29 (2007), 477-488. https://doi.org/10.4218/etrij.07.0106.0145
  8. E. R. Reyes et al., Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: application to process engineering design, Eng. Appl. Artif. Intell. 41 (2015), 1-16. https://doi.org/10.1016/j.engappai.2015.01.015
  9. D. Mourtzis, M. Doukas, and C. Giannoulis, An inference-based knowledge reuse framework for historical product and production information retrieval, Procedia CIRP 41 (2016), 472-477. https://doi.org/10.1016/j.procir.2015.12.026
  10. P. P. Ruiz, B. K. Foguem, and B. Grabot, Generating knowledge in maintenance from experience feedback, Knowl-Based Syst. 68 (2014), 4-20. https://doi.org/10.1016/j.knosys.2014.02.002
  11. W. L. Mikos et al., A system for distributed sharing and reuse of design and manufacturing knowledge in the PFMEA domain using a description logics-based ontology, J. Manuf. Syst. 30 (2011), 133-143. https://doi.org/10.1016/j.jmsy.2011.06.001
  12. J. Cheng, A strong relevant logic model of epistemic processes in scientific discovery, Information modelling and knowledge bases XI, IOS Press, Washington, DC, USA, 2000.
  13. M. H. Abel, Knowledge map-based web platform to facilitate organizational learning return of experiences, Comput. Human Behaver 51 (2015), 960-966. https://doi.org/10.1016/j.chb.2014.10.012
  14. L. Razmerita et al., Ontology-based user modeling for knowledge management systems, in Proc. Int. Conf. User Model., Johnstown, PA, USA, June 22-26, 2003, pp. 213-217.
  15. A. Osterwalder and Y. Pigneur, An eBusiness model ontology for modeling eBusiness, in Proc. BLED Electron. Commerce Conf., Bled, Slovenia, June 17-19, 2002, pp. 1-12.
  16. S. Zhang et al., Ontology-based semantic modeling of construction safety knowledge: towards automated safety planning for job hazard analysis (JHA), Automat. Constr. 52 (2015), 29-41. https://doi.org/10.1016/j.autcon.2015.02.005
  17. G. S. Stephan, H. S. Pascal, and A. S. Andreas, Knowledge representation and ontologies, Springer, Berlin, Heidelberg, 2007.
  18. T. R. Gruber, A translation approach to portable ontology specification, Knowl. Acquis. 5 (1993), 199-220. https://doi.org/10.1006/knac.1993.1008
  19. M. A. Musen, Dimensions of knowledge sharing and reuse, Comp. Biomed. Res. 25 (1992), 435-467. https://doi.org/10.1016/0010-4809(92)90003-S
  20. N. F. Noy and D. L. McGuinness, Ontology development 101: A guide to creating your first ontology, Stanford Knowledge Systems Laboratory Technical Report KSL‐01‐05 and Stanford Medical Informatics Technical Report SMI‐2001‐0880, Mar. 2001.
  21. D. L. McGuinness and J. Wright, Conceptual modeling for configuration: A description logic-based approach, Artif. Intell. Eng. Des. Anal. Manuf. 12 (1998), no. 4, 333-344. https://doi.org/10.1017/S089006049812406X
  22. D. L. McGuinness et al., An environment for merging and testing large ontologies, in Proc. Int. Conf. Principle Knowl. Representation Reasoning, Breckenridge, CO, USA, Apr. 11-15, 2000, pp. 483-493.
  23. P. L. Lee, What's wrong with logic models, LCSA: Occasional Paper no. 1, 2011, available: https://www.lcsansw.org.au/documents/item/210
  24. Q. Ni et al., A foundational ontology‐based model for human activity representation in smart homes, J. Ambient Intell. Smart Environ. 8 (2016), no. 1, 47-61. https://doi.org/10.3233/AIS-150359
  25. B. Smith, Logic and formal ontology, in J. N. Mohanty and W. McKenna (eds.), Husserl's phenomenology: a textbook, University Press of America, Lanham, MD, USA, 1989.
  26. J. Shen and Y. Ban, Route choice of the shortest travel time based on floating car data, J. Sens. 2016 (2016), 7041653:1-7041711.
  27. Y. Hu et al, A geo-ontology design pattern for semantic trajectories, in Proc Int. Conf. Spatial Inform. Theory, Scarborough, UK, Sept. 2-6, 2013, pp. 438-456.
  28. M. Effati and A. Sadeghi-Niaraki, A semantic-based classification and regression tree approach for modelling complex spatial rules in motor vehicle crashes domain, WIREs: Mining Knowl Discovery 5 (2015), no. 4, 181-194. https://doi.org/10.1002/widm.1152
  29. M. Baglioni et al., An ontology-based approach for the semantic modelling and reasoning on trajectories, in Proc. Int. Conf. Concept. Model., Barcelona, Spain, Oct. 20-23, 2008, pp. 344-353.
  30. U. Durak, H. Oguztuzun, and S. K. Ider, Ontology‐based domain engineering for trajectory simulation reuse, Int. J. Softw. Eng. Knowl. Eng. 19 (2009), no. 8, 1109-1129. https://doi.org/10.1142/S0218194009004532
  31. E. Camossi, P. Villa, and L. Mazzola, Semantic-based anomalous pattern discovery in moving object trajectories, arXiv preprint arXiv:1305.1946, 2013.
  32. R. Wannous et al., Modelling mobile object activities based on trajectory ontology rules considering spatial relationship rules, Modeling approaches and algorithms for advanced computer applications, Springer International Publishing, 2013, pp. 249-258.
  33. T. Malgundkar, M. Rao, and S. S. Mantha, GIS driven urban traffic analysis based on ontology, Int. J. Manag. Inform. Technol. 4 (2012), 15-23. https://doi.org/10.5121/ijmit.2012.4102
  34. A. Sadeghi-Niaraki et al., Ontology based SDI to facilitate spatially enabled society, in Proc. GSDI 12 World Conf., Singapore, Oct. 19-22, 2010, pp. 1-10.
  35. A. S. Niaraki and K. Kim, Ontology based personalized route planning system using a multi-criteria decision making approach, Expert Syst. Applicat. 36 (2009), no. 2, 2250-2259. https://doi.org/10.1016/j.eswa.2007.12.053
  36. S. Saeedi et al., An ontology based context modeling approach for mobile touring and navigation system, in Proc. Canadian Geomatics Conf. Symp. Commission I, ISPRS Convergence in Geomatics-Shaping Canada's Competitive Landscape, Calgary, Canada, June 15-18, 2010, pp. 1-7.

피인용 문헌

  1. An Improved Route-Finding Algorithm Using Ubiquitous Ontology-Based Experiences Modeling vol.2019, 2019, https://doi.org/10.1155/2019/9584397
  2. A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology vol.8, pp.7, 2019, https://doi.org/10.3390/electronics8070808