Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5459))

Included in the following conference series:

  • 814 Accesses

Abstract

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.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Liu, Y.: On Automatic Construction of Domain Ontology, Post-Doctor thesis, Peking University (2007)

    Google Scholar 

  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. Buitelaar, P., Cimiano, P., Grobelnik, M., Sintek, M.: Ontology Learning from Text. In: Tutorial at ECML/PKDD 2005 (2005)

    Google Scholar 

  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. Cimiano, P.: Ontology Learning and Population: Algorithms, Evaluation and Applications. PhD thesis, University of Karlsruhe (forthcoming, 2005)

    Google Scholar 

  6. Agichtein, E., Gravano, L.: Snowball: Extracting Relations from Large Plain-Text Collections. In: ACM DL (2000)

    Google Scholar 

  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. Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic Publishers, Boston (2002)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhao, Q., Sui, Z. (2009). An Integrated Approach for Concept Learning and Relation Extraction. In: Li, W., Mollá-Aliod, D. (eds) Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy. ICCPOL 2009. Lecture Notes in Computer Science(), vol 5459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00831-3_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-00831-3_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00830-6

  • Online ISBN: 978-3-642-00831-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics