Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Large-Scale Schema Matching

  • Erhard RahmEmail author
  • Eric Peukert
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_330-1



Schema matching aims at identifying semantically corresponding elements in two or more schemas, e.g., database schemas or ontologies. Large-scale schema matching focusses on the challenging cases of matching large schemas or more than two schemas. Matching multiple (>2) schemas is also known as holistic schema matching.


Schema matching aims at identifying semantic correspondences between metadata structures or models, such as database schemas, XML message formats, and ontologies. Solving such match problems is a key task in numerous application fields, in particular for data exchange and virtually all kinds of data integration. For example, in the two simple database schemas DB1.Student (Name, Major, Marks) and DB2.Grad-Student (LastName, FirstName, Major, Grades), simple (equivalence) correspondences would be DB1.Student ≈ DB2.Grad-Student, DB1.Major ≈ DB2.Major, and DB1.Marks ≈ DB2.Grades, while DB1.Name is related to both...

This is a preview of subscription content, log in to check access.


  1. Algergawy A, Massmann S, Rahm E (2011) A clustering-based approach for large-scale ontology matching. In: Proceedings of the advances in databases and information systems conference, vol 6909. Springer LNCSCrossRefGoogle Scholar
  2. Amin MB, Khan WA, Hussain S, Bui DM, Banos O, Kang BH, Lee S (2016) Evaluating large-scale biomedical ontology matching over parallel platforms. IETE Tech Rev 33(4):415–427CrossRefGoogle Scholar
  3. Arnold P, Rahm E (2014) Enriching ontology mappings with semantic relations. Data Knowl Eng 93:1–18CrossRefGoogle Scholar
  4. Do HH, Rahm E (2002) COMA – A system for flexible combination of sche-ma matching approaches. In: Proceedings of the very large data bases conference, pp 610–621CrossRefGoogle Scholar
  5. Do HH, Rahm E (2007) Matching large schemas: approaches and evaluation. Inf Syst 32(6):857–885CrossRefGoogle Scholar
  6. Dong XL, Srivastava D (2015) Big data integration. Morgan & Claypool, San RafaelGoogle Scholar
  7. Ehrig M, Staab S (2004) Quick ontology matching. In: Proceedings of the international conference semantic web, vol 3298. Springer LNCSGoogle Scholar
  8. Euzenat J, Shvaiko P (2013) Ontology matching, 2nd edn. Springer, Berlin/Heidelberg, GermanyCrossRefGoogle Scholar
  9. Gross A, Hartung M, Kirsten T, Rahm E (2010) On matching large life science ontologies in parallel. In: Proceedings of the data integration in the life sciences conference, vol 6254. Springer LNCSGoogle Scholar
  10. Gross A, Hartung M, Kirsten T and Rahm E (2011) Mapping composition for matching large life science ontologies. In: Proceedings of the international conference on biomedical ontology, pp 109–116Google Scholar
  11. Gross A, Hartung M, Kirsten T, Rahm E (2012) GOMMA results for OAEI 2012. In: Proceedings of the 7th ontology matching workshop. CEUR-WS 946Google Scholar
  12. Hanif MS, Aono M (2009) An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size. J Web Semant 7(4):344–356CrossRefGoogle Scholar
  13. Hu W et al (2008) Matching large ontologies: a divide-and-conquer-approach. Data Knowl Eng 67(1):140–160CrossRefGoogle Scholar
  14. Hung NQV, Tam NT, Miklós Z, Aberer K (2013) On leveraging crowdsourcing techniques for schema matching networks. In: Proceedings of the database systems for advanced applications, vol 7826. LNCS SpringerCrossRefGoogle Scholar
  15. Kolb L, Rahm E (2013) Parallel entity resolution with Dedoop. Datenbank-spektrum 13:23. SpringerCrossRefGoogle Scholar
  16. Madhavan J, Bernstein PA, Doan A, Halevy AY (2005) Corpus-based Schema matching. In: Proceedings of the IEEE ICDE conferenceGoogle Scholar
  17. Meilicke C, Stuckenschmidt H, Tamilin A (2007) Repairing ontology mappings. In: Proceedings of the AAAI, pp 1408–1413Google Scholar
  18. Peukert E, Berthold H, Rahm E (2010) Rewrite techniques for performance optimization of schema matching processes. In: Proceedings of the extending database technology conferenceGoogle Scholar
  19. Peukert E, Eberius J, Rahm E (2012) A self-configuring schema matching system. In: Proceedings of the IEEE ICDE conference, pp 306–317Google Scholar
  20. Pottinger RA, Bernstein PA (2003) Merging models based on given correspon- dences. In: Proceedings of the very large data bases conference, pp 862–873Google Scholar
  21. Rahm E (2011) Towards large-scale schema and ontology matching. In: Schema matching and mapping. Springer, Heidelberg, GermanyzbMATHGoogle Scholar
  22. Rahm E (2016) The case for holistic data integration. In: Proceedings of the advances in databases and information systems conference, vol 9809. Springer LNCS, Berlin/HeidelbergCrossRefGoogle Scholar
  23. Rahm E, Bernstein PA (2001) A survey of approaches to automatic schema matching. VLDB J 10(4):334–350CrossRefGoogle Scholar
  24. Raunich S, Rahm E (2014) Target-driven merging of taxonomies with ATOM. Inf Syst 42:1–14CrossRefGoogle Scholar
  25. Zhong Q, Li H et al (2009) A Gauss function based approach for unbalanced ontology matching. In: Proceedings of the ACM SIGMOD conferenceGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of LeipzigLeipzigGermany

Section editors and affiliations

  • Maik Thiele
    • 1
  1. 1.Database Systems GroupTechnische Universität DresdenDresdenDeutschland