Skip to main content

Semantic Matching

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems

Definition

Semantic matching: given two graph representations of ontologies G1 and G2, compute N1 × N2 mapping elements 〈IDi , j, n1i, n2j, R′〉, with \( n1_i\in \mathrm{G}1,\ i = 1,\ldots,\mathrm{N}1,\ n2_j \in \mathrm{G}2,\ j = 1,\ldots,\mathrm{N}2 \) and R′ the strongest semantic relation which is supposed to hold between the concepts at nodes n1i and n2j.

A mapping element is a 4-tuple 〈IDij, n1i, n2j, R〉, i = 1 , . . . , N1 ; j = 1 , . . . , N2; where IDij is a unique identifier of the given mapping element; n1i is the i-th node of the first graph, N1 is the number of nodes in the first graph; n2j is the j-th node of the second graph, N2 is the number of nodes in the second graph; and R specifies a semantic relation which is supposed to hold between the concepts at nodes n1i and n2j.

The semantic relations are within equivalence (=), more general (⊒), less general (⊑), disjointness (⊥) and overlapping(⊓). When none of the above mentioned relations can be explicitly computed, the...

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Avesani P, Giunchiglia F, Yatskevich M. A large scale taxonomy mapping evaluation. In: Proceedings of the 4th International Semantic Web Conference; 2005. p. 67–81.

    Chapter  Google Scholar 

  2. Batini C, Lenzerini M, Navathe S. A comparative analysis of methodologies for database schema integration. ACM Comput Surv. 1986;18(4):323–64.

    Article  Google Scholar 

  3. Bernstein P, Melnik S, Petropoulos M, Quix C. Industrial-strength schema matching. ACM SIGMOD Rec. 2004;33(4):38–43.

    Article  Google Scholar 

  4. Bouquet P, Serafini L, Zanobini S. Semantic coordination: a new approach and an application. In: Proceedings of the 2nd International Semantic Web Conference; 2003. p. 130–45.

    Google Scholar 

  5. Doan A, Madhavan J, Dhamankar R, Domingos P, Halevy AY. Learning to match ontologies on the semantic web. VLDB J. 2003;12(4):303–19.

    Article  Google Scholar 

  6. Euzenat J, Shvaiko P. Ontology matching. Berlin/New York: Springer; 2007.

    MATH  Google Scholar 

  7. Gal A. Why is schema matching tough and what can we do about it? ACM SIGMOD Rec. 2006;35(4):2–5.

    Article  Google Scholar 

  8. Gal A, Anaby-Tavor A, Trombetta A, Montesi D. A framework for modeling and evaluating automatic semantic reconciliation. VLDB J. 2005;14(1):50–67.

    Article  Google Scholar 

  9. Giunchiglia F, Marchese M, Zaihrayeu I. Encoding classifications into lightweight ontologies. J Data Semant. 2007;8:57–81.

    MATH  Google Scholar 

  10. Giunchiglia F, Shvaiko P. Semantic Matching. Knowl Eng Rev. 2003;18(3):265–80.

    Article  Google Scholar 

  11. Giunchiglia F, Shvaiko P, Yatskevich M. Discovering missing background knowledge in ontology matching. In: Proceedings of the 17th European Conference on Artificial Intelligence; 2006. p. 382–86.

    Google Scholar 

  12. Giunchiglia F, Yatskevich M, Avesani P, Shvaiko P. A large scale dataset for the evaluation of ontology matching systems. Knowl Eng Rev. 2008;23:1–22.

    Google Scholar 

  13. Giunchiglia F, Yatskevich M, Shvaiko P. Semantic matching: algorithms and implementation. J Data Semant. 2007;9:1–38.

    MATH  Google Scholar 

  14. Larson J, Navathe S, Elmasri R. A theory of attributed equivalence in databases with application to schema integration. IEEE Trans Softw Eng. 1989;15(4):449–63.

    Article  MATH  Google Scholar 

  15. Madhavan J, Bernstein P, Rahm E. Generic schema matching with Cupid. In: Proceedings of the 27th International Conference on Very Large Data Bases; 2001. p. 48–58.

    Google Scholar 

  16. Noy N, Musen M. The PROMPT suite: interactive tools for ontology merging and mapping. Int J Hum Comput Stud. 2003;59(6):983–1024.

    Article  Google Scholar 

  17. Rahm E, Bernstein P. A survey of approaches to automatic schema matching. VLDB J. 2001;10(4):334–50.

    Article  MATH  Google Scholar 

  18. Shvaiko P, Euzenat J. A survey of schema-based matching approaches. J Data Semant. 2005;4:146–71.

    MATH  Google Scholar 

  19. Spaccapietra S, Parent C. Conflicts and correspondence assertions in interoperable databases. ACM SIGMOD Rec. 1991;20(4):49–54.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pavel Shvaiko .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Giunchiglia, F., Shvaiko, P., Yatskevich, M. (2018). Semantic Matching. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1044

Download citation

Publish with us

Policies and ethics