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Discovering Semantic Sibling Groups from Web Documents with XTREEM-SG

  • Marko Brunzel
  • Myra Spiliopoulou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4248)

Abstract

The acquisition of explicit semantics is still a research challenge. Approaches for the extraction of semantics focus mostly on learning hierarchical hypernym-hyponym relations. The extraction of co-hyponym and co-meronym sibling semantics is performed to a much lesser extent, though they are not less important in ontology engineering.

In this paper we will describe and evaluate the XTREEM-SG (Xhtml TREE Mining – for Sibling Groups) approach on finding sibling semantics from semi-structured Web documents. XTREEM takes advantage of the added value of mark-up, available in web content, for grouping text siblings. We will show that this grouping is semantically meaningful. The XTREEM-SG approach has the advantage that it is domain and language independent; it does not rely on background knowledge, NLP software or training.

In this paper we apply the XTREEM-SG approach and evaluate against the reference semantics from two golden standard ontologies. We investigate how variations on input, parameters and reference influence the obtained results on structuring a closed vocabulary on sibling relations. Earlier methods that evaluate sibling relations against a golden standard report a 14.18% F-measure value. Our method improves this number into 21.47%.

Keywords

Vector Space Model Support Threshold Cluster Label Sibling Group Average Sibling 
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 2006

Authors and Affiliations

  • Marko Brunzel
    • 1
  • Myra Spiliopoulou
    • 1
  1. 1.Otto-von-Guericke-UniversityMagdeburg

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