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Approximating Geographical Queries

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Abstract

This article proposes a graph-theoretic methodology for query approximation in Geographic Information Systems, enabling the relaxation of three kinds of query constraints: topological, semantic and structural. An approximate query is associated with a value corresponding to the degree of similarity with the original query. Such a value is computed for topological constraints on the basis of the topological distance between configurations, for semantic constraints using the information content approach, and for structural constraints revisiting the maximum weighted matching problem in bipartite graphs. Finally, the high correlation of our proposal with human judgment is demonstrated by an experiment.

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References

  1. Ferri F, Rafanelli M. GeoPQL: A geographical pictorial query language that resolves ambiguities in query interpretation. Journal of Data Semantics III, Springer-Verlag Publ., LNCS 3534, 2005, pp.50–80.

  2. Egenhofer M J. Query processing in spatial-query-by-sketch. Journal of Visual Languages Computing, 1997, 8(4): 403–424.

    Article  Google Scholar 

  3. Bruns H T, Egenhofer M. Similarity of spatial scenes. In Proc the Seventh International Symposium on Spatial Data Handling, Kraak M J, Molenaar M (eds.), Delft, The Netherlands: Taylor & Francis, August 12–16, 1996, pp.31–42.

  4. Popper K R. The Logic of Scientific Discovery, London: Hutchinson, 1959.

  5. Egenhofer M J. Reasoning about binary topological relations. In Proc. the 2nd International Symposium on Large Spatial Databases (SSD1991), Z¨urich, Switzerland, August 28–30, 1991, LNCS 525, pp.143–160.

  6. Egenhofer M J, Franzosa R D. Point-set topological spatial relations. International Journal of Geographical Information Systems, 1991, 5(2): 161–174.

    Article  Google Scholar 

  7. Egenhofer M J, Sharma J. Topological relations between regions in R2 and Z2. In Proc. the 3rd International Symposium on Large Spatial Databases (SSD1993), Singapore, June 23–25, 1993, LNCS 692, pp.316–336.

  8. D’Ulizia A, Ferri F, Grifoni P, Rafanelli M. Relaxing constraints on GeoPQL operators for improving query answering. In Proc. the 17th International Conference on Database and Expert Systems Applications (DEXA2006), Krakow, Poland, Sept. 4–8, 2006, LNCS 4080, pp.728–737.

  9. WorldNet 2.1: A lexical database for the English language. 2005, http://www.cogsci.princeton.edu/cgi-bin/webwn.

  10. Lin D. An information-theoretic definition of similarity. In Proc. the 15th Int. Conference on Machine Learning (ICML 1998), Madison, USA, July 24–27, 1998, pp.296–304.

  11. Resnik P. Using information content to evaluate semantic similarity in a taxonomy. In Proc. the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995), Montréal, Québec, Canada, August 20–25, 1995, pp.448–453.

  12. Resnik P. Semantic similarity in a taxonomy: An informationbased measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 1999, 11: 95–130.

    MATH  Google Scholar 

  13. Storey V C. Understanding semantic relationships. VLDB Journal, 1993, 2(4): 455–488.

    Article  Google Scholar 

  14. Galil Z. Efficient algorithms for finding maximum matching in graphs. ACM Computing Surveys, 1986, 18(1): 23–38.

    Article  MATH  MathSciNet  Google Scholar 

  15. Güting R H. Geo-relational algebra: A model and query language for geometric database systems. In Proc. the International Conference on Extending Database Technology, Advances in Database Technology (EDBT1988), Schmidt J W, Ceri S, Missikoff M (eds.), Venice, Italy, March 14–18, 1988, pp.506–527.

  16. Lbath A, Aufaure-Portier M A, Laurini R. Using a visual language for the design and query in GIS customization. International IEEE Conference on Visual Information Systems (VISUAL 1997), San Diego, USA, Dec. 15–17, 1997, pp.197–204.

  17. Ross S. A First Course in Probability. Macmillan, 1976.

  18. Francis WN, Kucera H. Frequency Analysis of English Usage. Houghton Mifflin, Boston, 1982.

  19. Fellbaum C. A semantic network of English: The mother of all WordNets. Computers and the Humanities, 1998, 32(2/3): 209–220.

    Article  Google Scholar 

  20. Jiang J J, Conrath D W. Semantic similarity based on corpus statistics and lexical taxonomy. In Proc. ROCLING X, Taipei, China, August 22–24, 1997.

  21. Rada R, Mili H, Bicknell E, Blettner M. Development and application of a metric on semantic nets. IEEE Transaction on Systems, Man, and Cybernetics, 1989, 19(1): 17–30.

    Article  Google Scholar 

  22. Wu Z, Palmer M. Verb semantics and lexical selection. In Proc. the 32nd Annual Meeting of the Associations for Computational Linguistics, Las Cruces, USA, June 27–30, 1994, pp.133–138.

  23. Miller G A, Charles W G. Contextual correlates of semantic similarity. Language and Cognitive Processes, 1991, 6(1): 1–28.

    Article  Google Scholar 

  24. Schwering A. Approaches to semantic similarity measurement between geo-spatial data — A survey. Transactions in Geographic Information Science, Wilson J P, Fotheringham A S, Hunter G J (eds.), Blackwell Publishing, 2008, 12(1): 5–29.

  25. Janowicz K. Kinds of contexts and their impact on semantic similarity measurement. In Proc. the 5th IEEE Workshop on Context Modeling and Reasoning (CoMoRea 2008) at the 6th IEEE International Conference on Pervasive Computing and Communication (PerCom 2008), Hong Kong, China, IEEE Computer Society, March 17–21, 2008, pp.441–446.

  26. Bozkaya T, Ösoyoglu Z M. Indexing large metric spaces for similarity search queries. ACM Transactions on Database Systems, 1999, 24(3): 361–404.

    Article  Google Scholar 

  27. Barbara D, Dumouchel W, Faloutsos C, Haas P J, Hellerstein J M, Ioannidis Y E, Jagadish H V, Johnson T, Ng R T, Poosala V, Ross K A, Sevcik K C. The New Jersey data reduction report. IEEE Data Engineering Bulletin, 1997, 20(4): 3–45.

    Google Scholar 

  28. Han J, Kamber M. Data Mining: Concepts and Techniques. Academic Press, 2001.

  29. Egenhofer M J, Sharma J, Mark D M. A Critical Comparison of the 4-Intersection and 9-Intersection Models for Spatial Relations: Formal Analysis. In Proc. Autocarto 11, Minneapolis, USA, McMaster R, Armstrong M (eds.), Oct. 30–Nov. 1 1993, pp.1–11.

  30. Rodriguez A, Egenhofer M, Blaser A D. Query pre-processing of topological constraints: Comparing a composition-based with neighborhood-based approach. In Proc. the 8th International Symposium on Advances in Spatial and Temporal Databases (SSTD2003), Santorini Island, Greece, July 24–27, 2003, pp.362–379.

  31. Dylia F, Moratz R. Exploiting qualitative spatial neighborhoods in the situation calculus. In Proc. International Conference on Spatial Cognition, Bremen, Germany, Sept. 24–28, 2006, LNCS 3343, pp.304–322.

  32. Lee J H, Kim M H, Lee Y J. Information retrieval based on conceptual distance in is-a hierarchies. Journal of Documentation, 1989, 49(2): 188–207.

    Article  Google Scholar 

  33. Rodriguez A, Egenhofer M. Comparing geospatial entity classes: An asymmetric and context-dependent similarity measure. International Journal of Geographical Information Science (IJGIS), 2004, 18(3): 229–256.

    Article  Google Scholar 

  34. Maguitman A G, Menczer F, Roinestad H, Vespignani A. Algorithmic detection of semantic similarity. In Proc. the International World Wide Web Conference Committee (WWW2005), Chiba, Japan, 2005, pp.107–116.

  35. Frakes W B, Baeza-Yates R. Information Retrieval, Data Structure and Algorithms. Prentice Hall, 1992.

  36. Formica A, Missikoff M. Concept similarity in SymOntos: An enterprise ontology management tool. The Computer Journal, 2002, 45(6): 583–594.

    Article  MATH  Google Scholar 

  37. Formica A. Similarity of XML-schema elements: A structural and information content approach. The Computer Journal, 2008, 51(2): 240–254.

    Article  Google Scholar 

  38. Ferri F, Formica A, Grifoni P, Rafanelli M. Evaluating semantic similarity using GML in Geographic Information Systems. In Proc. the OTM’05 Workshops, Agia Napa, Cyprus, Springer-Verlag Publ., 2005, LNCS 3762, pp.1009–1019.

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Authors and Affiliations

  1. Institute of Research on Population and Social Policies, National Research Council, Rome, 00185, Italy

    Arianna D’Ulizia, Fernando Ferri & Patrizia Grifoni

  2. Institute for System Analysis and Computer Science, National Research Council, Rome, 00185, Italy

    Anna Formica

Authors
  1. Arianna D’Ulizia
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  2. Fernando Ferri
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  3. Anna Formica
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  4. Patrizia Grifoni
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Correspondence to Arianna D’Ulizia.

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D’Ulizia, A., Ferri, F., Formica, A. et al. Approximating Geographical Queries. J. Comput. Sci. Technol. 24, 1109–1124 (2009). https://doi.org/10.1007/s11390-009-9284-6

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  • DOI: https://doi.org/10.1007/s11390-009-9284-6

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