Matching Strategies

  • Jérôme Euzenat
  • Pavel Shvaiko


The basic techniques presented in Chap.  5 and the global techniques provided in Chap.  6 are the building blocks on which a matching system is built. Once the similarity or dissimilarity between ontology entities is available, the alignment remains to be computed. This involves more comprehensive treatments. In particular, the following aspects of building a working matching system are considered in this chapter:
  • preparing, if necessary, to handle large scale ontologies (Sect. 7.1.1),

  • organising the combination of various similarities or matching algorithms (Sect. 7.2),

  • exploiting background knowledge sources (Sect. 7.3),

  • aggregating the results of the basic methods in order to compute the compound similarity between entities (Sect. 7.4),

  • learning matchers from data (Sect. 7.5) and tuning them (Sect. 7.6),

  • extracting alignments from the resulting (dis)similarity: indeed, different alignments with different characteristics may be extracted from the same (dis)similarity (Sect. 7.7),

  • improving alignments through disambiguation, debugging and repair (Sect. 7.8).


Stable Marriage Ontology Match Triangular Norm Basic Matcher Maximum Weight Match 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jérôme Euzenat
    • 1
  • Pavel Shvaiko
    • 2
  1. 1.INRIA and LIGGrenobleFrance
  2. 2.Informatica Trentina SpA, while at Department of Engineering and Computer Science (DISI), University of Trento, while at Web of Data, Bruno Kessler Foundation - IRSTTrentoItaly

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