Ontology-Based Data Sharing in P2P Databases

  • Dimitrios Skoutas
  • Verena Kantere
  • Alkis Simitsis
  • Timos Sellis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5005)


We consider peer-to-peer systems in which peers share structured data through the use of schema mappings. Peers express their queries and rewrite incoming queries on their local schema. We assume the existence of one or more ontologies describing the domain of interest of the peers. The ontologies are used to semantically annotate each peer schema, making explicit the type of information provided by it. A major problem in such a system is that peers cannot easily judge the semantic relativeness of their interests to interests of other peers, as these are expressed by the respective local schemas. Moreover, peers cannot evaluate the semantic relativeness of answers that they receive to their queries. In this paper, we propose a semantic similarity measure for evaluating the semantic relativeness between peer schemas, as well as between queries and their rewritten versions on other peers. The similarity measure is first introduced under the assumption of a shared ontology among the community of peers, and then it is extended, employing ontology matching and translation techniques, to support the comparison of class expressions accross multiple ontologies. The proposed similarity measure adopts the notions of recall and precision from the field of Information Retrieval. Our goal is to use this measure for the identification of semantically relevant peers and the evaluation of the quality of the received answers based on the semantic annotations, the mappings, and the queries issued.


Semantic Similarity Description Logic Domain Ontology Semantic Annotation Original Query 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arenas, M., Kantere, V., Kementsietsidis, A., Kiringa, I., Miller, R.J., Mylopoulos, J.: The hyperion project: from data integration to data coordination. SIGMOD Record 32(3), 53–58 (2003)CrossRefGoogle Scholar
  2. 2.
    Halevy, A., Ives, Z., Suciu, D., Tatarinov, I.: Schema Mediation in Peer Data Management Systems. In: ICDE (2003)Google Scholar
  3. 3.
    Halevy, A.Y.: Answering Queries Using Views: A Survey. VLDB J 10(4), 270–294 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Kantere, et al.: V.: Coordinating P2P Databases Using ECA Rules. In: DBISP2P (2003)Google Scholar
  5. 5.
    Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)CrossRefGoogle Scholar
  6. 6.
    Borst, W.N.: Construction of Engineering Ontologies for Knowledge Sharing and Reuse. PhD thesis, University of Twente, Enschede, The Netherlands (1997)Google Scholar
  7. 7.
    McGuinness, D.L., van Harmelen, F.: OWL Web Ontology Language Overview. W3C Recommendation, W3C (February 2004),
  8. 8.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  9. 9.
    Patel-Schneider, P.F., Horrocks, I.: OWL 1.1 Web Ontology Language. W3C Member Submission, W3C (December 2006)Google Scholar
  10. 10.
    Pan, J.Z., Horrocks, I.: OWL-Eu: Adding customised datatypes into owl. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Lenzerini, M.: Data Integration: A Theoretical Perspective. In: PODS, pp. 233–246 (2002)Google Scholar
  12. 12.
    Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval. ACM Press / Addison-Wesley (1999)Google Scholar
  13. 13.
    Lin, D.: An Information-Theoretic Definition of Similarity. In: ICML (1998)Google Scholar
  14. 14.
    Resnik, P.: Using Information Content to Evaluate Semantic Similarity in a Taxonomy. In: IJCAI, pp. 448–453 (1995)Google Scholar
  15. 15.
    Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. J. Data Semantics IV, 146–171 (2005)Google Scholar
  16. 16.
    Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A comparison of string metrics for matching names and records. In: Proceedings of the KDD-2003 Workshop on Data, Washington, DC, pp. 13–18 (2003)Google Scholar
  17. 17.
    Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  18. 18.
    Dou, D., McDermott, D.V., Qi, P.: Ontology translation on the semantic web. J. Data Semantics 2, 35–57 (2005)CrossRefGoogle Scholar
  19. 19.
    Noy, N.F., Musen, M.A.: Prompt: Algorithm and tool for automated ontology merging and alignment. In: AAAI/IAAI, pp. 450–455. AAAI Press / The MIT Press (2000)Google Scholar
  20. 20.
    Mota, L., Botelho, L.: Owl ontology translation for the semantic web. In: Proceedings of the Semantic Computing Workshop of the 14th International World Wide Web Conference (2005)Google Scholar
  21. 21.
    Beeri, C., Levy, A.Y., Rousset, M.C.: Rewriting queries using views in description logics. In: PODS, pp. 99–108. ACM Press, New York (1997)CrossRefGoogle Scholar
  22. 22.
    Crespo, A., Garcia-Molina, H.: Semantic Overlay Networks for P2P Systems. In: Moro, G., Bergamaschi, S., Aberer, K. (eds.) AP2PC 2004. LNCS (LNAI), vol. 3601, pp. 1–13. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  23. 23.
    Sripanidkulchai, K., Maggs, B., Zhang, H.: Efficient Content Location Using Interest-Based Locality in Peer-to-Peer Systems. In: INFOCOM (2003)Google Scholar
  24. 24.
    Voulgaris, S., et al.: Exploiting Semantic Proximity in Peer-to-Peer Content Searching. In: FTDCS (2004)Google Scholar
  25. 25.
    Handurukande, S., et al.: Exploiting Semantic Clustering in the eDonkey P2P Network. In: ACM SIGOPS (2004)Google Scholar
  26. 26.
    Kokkinidis, G., Sidirourgos, E., Christophides, V.: Query Processing in RDF/S-based P2P Database Systems. In: Semantic Web and Peer-to-Peer, pp. 59–81. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  27. 27.
    Aberer, K., Cudre-Mauroux, P., Hauswirth, M., Pelt, T.V.: Gridvine:Building internet-scale semantic overlay networks. In: ISWC (2004)Google Scholar
  28. 28.
    Tang, C., et al.: Peer-to-Peer Information Retrieval Using Self-Organizing Semantic Overlay Networks. In: SIGCOMM (2003)Google Scholar
  29. 29.
    Haase, P., et al.: Bibster - A Semantics-based Bibliographic Peer-to-Peer System. Journal of Web Semantics 2(1), 99–103 (2005)MathSciNetGoogle Scholar
  30. 30.
    Agrawal, S., Chaudhuri, S., Das, G.: DBXplorer: A System for Keyword-Based Search over Relational Databases. In: ICDE (2002)Google Scholar
  31. 31.
    Cohen, W.: Integration of Heterogeneous Databases Without Common Domains Using Queries Based on Textual Similarity. In: SIGMOD (1998)Google Scholar
  32. 32.
    Fuhr, N.: A Probabilistic Framework for Vague Queries and Imprecise Information in Databases. In: VLDB (1990)Google Scholar
  33. 33.
    Kießling, W., Kostner, G.: Preference SQL - Design, Implementation, Experiences. In: VLDB (2002)Google Scholar
  34. 34.
    Motro, A.: VAGUE: A User Interface to Relational Databases that Permis Vague Queries. In: TOIS, vol. 6(3), pp. 187–214 (1988)Google Scholar
  35. 35.
    Agrawal, S., Chaudhuri, S., Das, G., Gionis, A.: Automated Ranking of Database Query Results. In: CIDR (2003)Google Scholar
  36. 36.
    Ghosh, A., Parikh, J., Sengar, V.S., Haritsa, J.R.: Plan Selection based on Query Clustering. In: VLDB (2002)Google Scholar
  37. 37.
    Chu, W.W., Zhang, G.: Associative Query Answering via Query Feature Similarity. In: IIS (1997)Google Scholar
  38. 38.
    Mandreoni, F., Martoglia, R., Sassateli, S., Penzo, W.: SRI: Exploitng Semantic Information for Effective Query Routing in a PDMS. In: WIDM (2006)Google Scholar
  39. 39.
    Aberer, K., Cudré-Mauroux, P., Hauswirth, M.: Start making sense: The chatty web approach for global semantic agreements. J. Web Sem. 1(1), 89–114 (2003)Google Scholar
  40. 40.
    Janowicz, K.: Sim-dl: Towards a semantic similarity measurement theory for the description logic cnr in geographic information retrieval. In: OTM Workshops, vol. (2) (2006)Google Scholar
  41. 41.
    Maguitman, A.G., et al.: Algorithmic computation and approximation of semantic similarity. In: World Wide Web, vol. 9(4), pp. 431–456 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dimitrios Skoutas
    • 1
    • 2
  • Verena Kantere
    • 1
  • Alkis Simitsis
    • 3
  • Timos Sellis
    • 1
    • 2
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensAthens 
  2. 2.Institute for the Management of Information Systems (R.C. “Athena”) Athens 
  3. 3.Stanford University, Palo AltoUSA

Personalised recommendations