An Integrated Approach for Concept Learning and Relation Extraction

  • Qingliang Zhao
  • Zhifang Sui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)


Concept learning and hierarchical relations extraction are core tasks of ontology automatic construction. In the current research, the two tasks are carried out separately, which separates the natural association between them. This paper proposes an integrated approach to do the two tasks together. The attribute values of concepts are used to evaluate the extracted hierarchical relations. On the other hand, the extracted hierarchical relations are used to expand and evaluate the attribute values of concepts. Since the interaction is based on the inaccurate result that extracted automatically, we introduce the weight of intermediate results of both tasks into the iteration to ensure the accuracy of results. Experiments have been carried out to compare the integrated approach with the separated ones for concept learning and hierarchical relations. Our experiments show performance improvements in both tasks.


Ontology Integrated Approach Concept Learning Attribute Values Extraction Hierarchical Relations Extraction 


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  1. 1.
    Liu, Y.: On Automatic Construction of Domain Ontology, Post-Doctor thesis, Peking University (2007)Google Scholar
  2. 2.
    Sato, S., Sasaki, Y.: Automatic collection of related terms from the web. IPSJ SIG Notes 2003(4), 57–64, 20030120 (2003)Google Scholar
  3. 3.
    Buitelaar, P., Cimiano, P., Grobelnik, M., Sintek, M.: Ontology Learning from Text. In: Tutorial at ECML/PKDD 2005 (2005)Google Scholar
  4. 4.
    Cimiano, P., Staab, S.: Learning Concept Hierarchies from Text with a Guided Agglomerative Clustering Algorithm. In: Proceedings of the ICML 2005 Workshop on Learning and Extending Lexical Ontologies with Machine Learning Methods (2005)Google Scholar
  5. 5.
    Cimiano, P.: Ontology Learning and Population: Algorithms, Evaluation and Applications. PhD thesis, University of Karlsruhe (forthcoming, 2005)Google Scholar
  6. 6.
    Agichtein, E., Gravano, L.: Snowball: Extracting Relations from Large Plain-Text Collections. In: ACM DL (2000)Google Scholar
  7. 7.
    Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. J. Artificial Intelligence Research 24, 305–339 (2005)Google Scholar
  8. 8.
    Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic Publishers, Boston (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Qingliang Zhao
    • 1
  • Zhifang Sui
    • 1
  1. 1.Institute of Computational LinguisticsPeking UniversityBeijingPR China

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