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Content based similarity of geographic classes organized as partition hierarchies

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Abstract

In this paper we propose a method to measure the semantic similarity of geographic classes organized as partition hierarchies within Naive Geography. The contribution of this work consists in extending and integrating the information content approach, and the method for comparing concept attributes in the ontology management system SymOntos developed at IASI. As a result, this proposal allows us to address both the concept similarity within the partition hierarchy, and the attribute similarity of geographic classes and, therefore, to reduce the gap among the different similarity approaches defined in the literature.

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References

  1. Beeri C (1990) A formal approach to object-oriented databases. Data Knowl Eng 5: 353–382

    Article  Google Scholar 

  2. Berry MW, Letsche TA (1997) Large-scale information retrieval with latent semantic indexing. Inform Sci 100(1–4): 105–137

    Google Scholar 

  3. Bruns T, Egenhofer MJ (1996) Similarity of spatial scenes. In: Kraak MJ, Molenaar M (eds) Seventh International Symposium on Spatial Data Handling, Delft, The Netherlands, pp 31–42

  4. Castano S, De Antonellis V, Fugini MG, Pernici B (1998) Conceptual schema analysis: techniques and applications. ACM Trans Database Syst 23(3): 286–332

    Article  Google Scholar 

  5. Castano S, Ferrara A, Hess GN (2006) Discovery-driven ontology evolution, semantic web applications and perspectives (SWAP), 18–20 December, Pisa, Italy

  6. Chiang T, Tsai T (2008) Querying color images using user-specified wavelet features. Knowl Inform Syst 15(1): 109–129

    Article  MathSciNet  Google Scholar 

  7. Cobb M, Chung M, Foley H, Petry F, Shaw K, Miller V (1998) A rule-based approach for the conflation of attributed vector data. GeoInformatica 2(1): 7–35

    Article  Google Scholar 

  8. Codd EF (1979) Extending the database relational model to capture more meaning. ACM Trans Database Syst 4(4): 397–434

    Article  Google Scholar 

  9. Egenhofer MJ, Franzosa R (1991) Point set topological spatial relations. Int J Geogr Inform Syst 5: 161–174

    Article  Google Scholar 

  10. Egenhofer MJ, Franzosa R (1995) On the equivalence of topological relations. Int J Geogr Inform Syst 9(2): 133–152

    Article  Google Scholar 

  11. Egenhofer MJ, Mark DM (1995) Naive geography. In: Spatial information theory: a theoretical basis for GIS, International Conference COSIT, Semmering, Austria, September 21–23, 1995. Lecture notes in computer science, vol 988. Springer, Berlin, pp 1–15

  12. Egenhofer MJ, Rashid A, Shariff BM (1998) Metric details for natural-language spatial relations. ACM Trans Inform Syst 16(4): 295–321

    Article  Google Scholar 

  13. Ehrig M, Haase P, Hefke M, Stojanovic N (2005) Similarity for ontologies—a comprehensive framework. In: Proc. of European Conference on Information Systems (ECIS), Regensburg, Germany

  14. Fellbaum C (1998) A semantic network of English: the mother of all WordNets. Comput Humanit 32: 209–220

    Article  Google Scholar 

  15. Formica A, Missikoff M (2002) Concept similarity in SymOntos: an enterprise ontology management tool. Comput J 45(6): 583–594

    Article  MATH  Google Scholar 

  16. Formica A, Missikoff M (2004) Inheritance processing and conflicts in structural generalization hierarchies. ACM Comput Surv 36(3): 263–290

    Article  Google Scholar 

  17. Francis WN, Kucera H (1982) Frequency analysis of English usage. Houghton Mifflin, Boston

    Google Scholar 

  18. Frank AU, Volta GS, McGranaghan M (1997) Formalization of families of categorical coverages. Int J Geogr Inform Sci 11(3): 215–231

    Article  Google Scholar 

  19. Galil Z (1986) Efficient algorithms for finding maximum matching in graphs. ACM Comput Surv 18(1): 23–38

    Article  MATH  MathSciNet  Google Scholar 

  20. Goyal RK, Egenhofer MJ (2001) Similarity of cardinal directions. In: Proc. of Seventh International Symposium on Spatial and Temporal Databases (SSTD), Redondo Beach, CA, pp 36–58

  21. Harvey F (1999) Designing for interoperability: overcoming semantic differences. In: Goodchild MF, Egenhofer MJ, Fegeas R, Kottman CA(eds) Interoperating Geographic Information Systems. Boston Kluwer, Dordrecht, pp 58–98

    Google Scholar 

  22. Hsu CC, Chu WW, Taira RK (1996) A knowledge-based approach for retrieving images by content. IEEE Trans Knowl Data Eng 8(4): 522–532

    Article  Google Scholar 

  23. Jiang JJ, Conrath DW (1997) Semantic similarity based on corpus statistics and lexical taxonomy. In: Proc. of the 10th International Conference on Research in Computational Linguistics (ROCLING), Taiwan, pp 1–15

  24. Jurisica I, Mylopoulos J, Yu E (2004) Ontologies for knowledge management: an information systems perspective. Knowl Inform Syst 6(4): 380–401

    Article  Google Scholar 

  25. Kavouras M, Kokla M, Tomai E (2005) Comparing categories among geographic ontologies. Comput Geosci (special issue) 31: 145–154

    Article  Google Scholar 

  26. Lee JH, Kim MH, Lee YJ (1993) Information retrieval based on conceptual distance in IS-A hierarchies. J Doc 49(2): 188–207

    Article  Google Scholar 

  27. Lin D (1998) An Information-theoretic definition of similarity. In: Shavlik JW (ed) Proc. of 15th the International Conference on Machine Learning, Madison, Wisconsin, USA, Morgan Kaufmann, pp 296–304

  28. Maarek YS, Berry DM, Kaiser GE (1991) An information retrieval approach for automatically constructing software libraries. IEEE Trans Softw Eng 17(8): 800–813

    Article  Google Scholar 

  29. McIntosh J, Yuan M (2005) Assessing similarity of geographic processes and events. Trans GIS 9(2): 223–245

    Article  Google Scholar 

  30. Miller GA, Charles WG (1991) Contextual correlates of semantic similarity. Lang Cogn Process 6(1): 1–28

    Article  Google Scholar 

  31. Navigli R, Velardi P (2004) Learning domain ontologies from document warehouses and dedicated web sites. Comput Linguist 30(2): 151–179

    Article  Google Scholar 

  32. Nedas KA, Egenhofer MJ (2003) Spatial similarity queries with logical operators. In: Int. Symposium on Spatial and Temporal Databases (SSTD). Lecture notes in computer science, vol 2750, pp 430–448

  33. Papadias D, Delis V (1997) Relation-based similarity. In: Proc. of the 5th ACM Int. workshop on Advances in Geographical Information Systems, Las Vegas, Nevada. ACM Press, New York, pp 1–4

  34. Pinto HS, Martins JP (2004) Ontologies: how can they be built?. Knowl Inform Syst 6(4): 441–464

    Article  Google Scholar 

  35. Pourabbas E (2003) Cooperation with geographic databases. In: Rafanelli M(eds) Multidimensional Databases: Problems and Solutions Idea Group Inc.. IGP/INFOSCI/IRM Press, Hershey, pp 393–432

    Google Scholar 

  36. Rada L, Mili H, Bicknell E, Bletter M (1989) Development and application of a metric on semantic nets. IEEE Trans Syst Man Cybern 19(1): 17–30

    Article  Google Scholar 

  37. Rasmussen E (1992) Clustering algorithms. In: Frakes WB, Baeza-Yates R(eds) Information retrieval: data structures & algorithms.. Prentice-Hall, Englewood Cliffs, pp 419–442

    Google Scholar 

  38. Resnik P (1995) Using information content to evaluate semantic similarity in a taxonomy. In: Proc. of the Int. Joint Conference on Artificial Intelligence (IJCAI), Montréal, Québec, Canada, August 20–25. Morgan-Kaufmann, Menlo Park, pp 448–453

  39. Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res (JAIR) 11: 95–130

    MATH  Google Scholar 

  40. Rigaux P, Scholl M (1994) Multiple representation modelling and querying. In: Nievergelt J, Roos T, Schek HJ, Widmayer P(eds) Proc. of the International Workshop on Advanced Information Systems: Geographic Information System. Lecture notes in computer science, vol 884.. Springer, Berlin, pp 59–69

    Google Scholar 

  41. Rodriguez A, Egenhofer M (2003) Determining semantic similarity among entity classes from different ontologies. IEEE Trans Knowl Data Eng 15(2): 442–456

    Article  Google Scholar 

  42. Rodriguez A, Egenhofer M (2004) Comparing geospatial entity classes: an asymmetric and context-dependent similarity measure. Int J Geogr Inform Sci 18(3): 229–256

    Article  Google Scholar 

  43. Ross S (1976) A first course in probability. Macmillan, New York

    MATH  Google Scholar 

  44. Samet H (2004) Indexing issues in supporting similarity searching. In: Advances in Multimedia Information Processing, PCM 2004. Lecture notes in computer science, vol 3332. Springer, Berlin, pp 463–470

  45. Storey VC (1993) Understanding semantic relationships. VLDB J 2(4): 455–488

    Article  Google Scholar 

  46. Tan H, Lambrix P (2007) A method for recommending ontology alignment strategies. In: The 6th International Semantic Web Conference—The 2nd Asian Semantic Web Conference, Busan, Korea, November 11–15

  47. Tombros A, van Rijsbergen CJ (2004) Query-sensitive similarity measures for information retrieval. Knowl Inform Syst 6(5): 617–642

    Article  Google Scholar 

  48. Teorey TL, Yang D, Fry JP (1986) A logical design methodology for relational databases using the extended entity-relational model. ACM Comput Surv 18(2): 197–222

    Article  MATH  Google Scholar 

  49. Tsichritzis D, Lochovsky F (1982) Data models. Prentice-Hall, New York

    Google Scholar 

  50. Tversky A (1977) Features of similarity. Psychol Rev 84(4): 327–352

    Article  Google Scholar 

  51. Winston ME, Chaffin R, Hermann D (1987) A taxonomy of part-whole relations. Cogn Sci 11: 417–444

    Article  Google Scholar 

  52. WordNet 2.0: A lexical database for the English language. http://www.cogsci.princeton.edu/cgi-bin/webwn

  53. Yeh A, Shi X (2003) The application of case-based reasoning in development control. In: Geertman S, Stillwell J(eds) Planning support systems in practice. Springer, Berlin, pp 223–248

    Google Scholar 

  54. Zhang K, Tang J, Hong M, Li J, Wei W (2006) Weighted ontology-based search exploiting semantic similarity. In: Zhou X et al (eds) Frontiers of WWW Research and Development, APWeb 2006. Lecture notes in computer science, vol 3841, pp 498–510

  55. Zhou M, Wong M, Chu K (2006) A geometrical solution to time series searching invariant to shifting and scaling. Knowl Inform Syst 9(2): 202–229

    Article  Google Scholar 

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Authors and Affiliations

  1. Istituto di Analisi dei Sistemi ed Informatica (IASI) “Antonio Ruberti”, National Research Council, Viale Manzoni 30, 00185, Rome, Italy

    Anna Formica & Elaheh Pourabbas

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  1. Anna Formica
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  2. Elaheh Pourabbas
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Correspondence to Elaheh Pourabbas.

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Formica, A., Pourabbas, E. Content based similarity of geographic classes organized as partition hierarchies. Knowl Inf Syst 20, 221–241 (2009). https://doi.org/10.1007/s10115-008-0177-8

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