Matching Formal and Informal Geospatial Ontologies

  • Heshan DuEmail author
  • Natasha Alechina
  • Mike Jackson
  • Glen Hart
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


The rapid development of crowd-sourcing or volunteered geographic information both challenges and provides opportunities to authoritative geospatial information. Matching geospatial ontologies is an essential element to realizing the synergistic use of disparate geospatial information. We propose a new semi-automatic method to match formal and informal real life geospatial ontologies, at both terminology level and instance level, ensuring that overall information is logically coherent and consistent. Disparate geospatial ontologies are matched by finding a consistent and coherent set of mapping axioms with respect to them. Disjointness axioms are generated in order to facilitate detection of errors. In contrast to other existing methods, disjointness axioms are seen as assumptions, which can be retracted during the overall process. We produce candidates for retraction automatically, but the ultimate decision is taken by domain experts. Geometry matching, lexical matching and cardinality checking are combined when matching geospatial individuals (spatial features).


Domain Expert Geospatial Information Biomedical Ontology Ontology Match Local Ontology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF (eds) (2007) The description logic handbook. Cambridge University Press, CambridgeGoogle Scholar
  2. Bouquet P (2007) Contexts and ontologies in schema matching. Context and ontology representation and reasoning. Roskilde University, DenmarkGoogle Scholar
  3. Bouquet P, Serafini L, Zanobini S (2003) Semantic coordination: a new approach and an application. International semantic web conference, pp 130–145Google Scholar
  4. Buccella A, Cechich A, Fillottrani P (2009) Ontology-driven geographic information integration: a survey of current approaches. Comput Geosci 35:710–723CrossRefGoogle Scholar
  5. Domingos P, Lowd D, Kok S, Poon H, Richardson M, Singla P (2008) Just add weights: markov logic for the semantic web. Uncertainty reasoning for the semantic web I, ISWC International Workshops, URSW 2005–2007, Revised Selected and Invited Papers, 2008, pp 1–25Google Scholar
  6. Du H, Anand S, Alechina N, Morley J, Hart G, Leibovici D, Jackson M, Ware M (2012) Geospatial information integration for authoritative and crowd sourced road vector data. Transactions in GIS, Blackwell Publishing Ltd, 2012, 16, 455–476Google Scholar
  7. Euzenat J, Shvaiko P (2007) Ontology matching. Springer, BerlinGoogle Scholar
  8. Geofabrik GmbH Karlsruhe: Geofabrik (2012)
  9. Giunchiglia F, Dutta B, Maltese V, Farazi F (2012) A facet-based methodology for the construction of a large-scale geospatial ontology. J Data Semant 1:57–73 SpringerCrossRefGoogle Scholar
  10. Giunchiglia F, Shvaiko P, Yatskevich M (2004) S-Match: an algorithm and an implementation of semantic matching. European semantic web conference (ESWC), pp 61–75Google Scholar
  11. Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquisition 5:199–220CrossRefGoogle Scholar
  12. Gurobi Optimization Inc (2012) Gurobi optimizer reference manual.
  13. Hart G, Dolbear C, Kovacs K, Guy A (2008) Ordnance survey ontologies.
  14. International Organization for Standardization (2011) ISO/DIS 19157: Geographic information—Data qualityGoogle Scholar
  15. Jackson MJ, Rahemtulla H, Morley J (2010) The synergistic use of authenticated and crowd-sourced data for emergency response. The 2nd international workshop on validation of geo-information products for crisis management (VALgEO). Ispra, Italy, pp 91–99, 11–13 Oct 2010. Available online:
  16. Jain P, Hitzler P, Sheth AP, Verma K, Yeh PZ (2010) Ontology alignment for linked open data. Int Semant Web Conf 1:402–417Google Scholar
  17. Jean-Mary YR, Shironoshita EP, Kabuka MR (2010) ASMOV: results for OAEI 2010. The 5th international workshop on ontology matching (OM-2010)Google Scholar
  18. Jiménez-Ruiz E, Grau BC (2011) LogMap: logic-based and scalable ontology matching. Int Semant Web Conf 1:273–288Google Scholar
  19. Jiménez-Ruiz E, Grau BC, Horrocks I, Llavori RB (2009) Ontology integration using mappings: towards getting the right logical consequences. The 6th european semantic web conference (ESWC), pp 173–187Google Scholar
  20. Meilicke C, Stuckenschmidt H (2009) An efficient method for computing alignment diagnoses. In: Third international conference on web reasoning and rule systems, pp 182–196Google Scholar
  21. Meilicke C, Stuckenschmidt H, Tamilin A (2008a) Reasoning support for mapping revision. J Logic Comput 19:807–829CrossRefGoogle Scholar
  22. Meilicke C, Völker J, Stuckenschmidt H (2008b) Learning disjointness for debugging mappings between lightweight ontologies. Proceedings of the 16th international conference on knowledge engineering: practice and patterns, Springer, Berlin, pp 93–108Google Scholar
  23. Niepert M, Meilicke C, Stuckenschmidt H (2010) A probabilistic-logical framework for ontology matching. American association for artificial intelligence for ontology matching. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence, Atlanta, Georgia, AAAI Press Google Scholar
  24. Nikolov A, Uren V, Motta E (2007) KnoFuss: a comprehensive architecture for knowledge fusion. The 4th international conference on knowledge capture, ACM, NY, pp 185–186Google Scholar
  25. OpenStreetMap (2012) The Free Wiki World Map.
  26. Ordnance Survey (2012) Ordnance Survey.
  27. Qi G, Ji Q, Haase P (2009) A conflict-based operator for mapping revision: theory and implementation. In: Proceedings of the 8th international semantic web conference. ISWC ‘09, Springer, Berlin, Heidelberg, pp 521–536Google Scholar
  28. Reul Q, Pan JZ (2010) KOSIMap: use of description logic reasoning to align heterogeneous ontologies. The 23rd international workshop on description logics (DL 2010)Google Scholar
  29. Sais F, Pernelle N, Rousset MC (2007) L2R: A logical method for reference reconciliation. In: AAAI conference on artificial intelligence. pp 329–334Google Scholar
  30. Scharffe F, Liu Y, Zhou C (2009) RDF-AI: an architecture for RDF datasets matching, fusion and interlink. IJCAI 2009 workshop on Identity, Reference and Knowledge Representation (IR-KR)Google Scholar
  31. Shvaiko P, Euzenat J (2012) Ontology matching: state of the art and future challenges. IEEE Transactions on Knowledge and Data EngineeringGoogle Scholar
  32. Sirin E, Parsia B, Grau BC, Kalyanpur A, Katz Y (2007) Pellet: a practical OWL-DL reasoner. Web semantics: science, services and agents on the World Wide Web, Elsevier Science Publishers B. V., vol 5, pp 51–53Google Scholar
  33. Volz S, Walter V (2004) Linking different geospatial databases by explicit relations. International society for photogrammetry and remote sensing (ISPRS) congress, communication vol IV, pp 152–157Google Scholar
  34. W3C (2009) OWL 2 Web Ontology Language.
  35. Wang P, Xu B (2008) Debugging ontology mappings: a static approach. Comput Artif Intell 27(1):21–36Google Scholar
  36. Wolger S, Siorpaes K, Bürger T, Simperl E, Thaler S, Hofer C (2011) A survey on data interlinking methods. Semantic Technology Institute (STI) Innsbruck, University of Innsbruck. Available online:

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Heshan Du
    • 1
    Email author
  • Natasha Alechina
    • 1
  • Mike Jackson
    • 1
  • Glen Hart
    • 2
  1. 1.The University of NottinghamNottinghamUK
  2. 2.Ordnance Survey of Great BritainSouthamptonUK

Personalised recommendations