An Ontology-Based Approach to Extracting Semantic Relations from Descriptive Text

  • Da Huang
  • Wei Hu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 406)


Linked Data have advantages over plain text, as data are organized in relations between information, which is convenient for learning and reasoning. However, most plain text with valuable information has not been converted into Linked Data form. Thus, we propose an ontology-based method to extract semantic relations from descriptive text about entities. Moreover, we conduct our experiment on the DBpedia dataset and design an automatic methodology to evaluate our ontology-based method as well as an intuitive method. As a result, we find out that our ontology-based method performs better than the intuitive one in general. At last, we analyze the results, and put forward our opinions on the difference between the two methods’ performance.


Semantic Relation Link Data Dependency Graph Descriptive Text Parse Tree 
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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  1. 1.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked Data – The Story So Far. International Journal on Semantic Web and Information Systems 5(3), 1–22 (2009)CrossRefGoogle Scholar
  2. 2.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: A Nucleus for a Web of Open Data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Buitelaar, P., Cimiano, P., Magnini, B.: Ontology Learning from Text: An Overview. ACM Computer Surveys. In: Breuker, J., et al. (eds.) Ontology Learning from Text: Methods, Evaluation and Applications. Frontiers in Artificial Intelligence and Applications, vol. 123, pp. 3–12. IOS Press, Amsterdam (2005)Google Scholar
  4. 4.
    Vela, M., Declerck, T.: A Methodology for Ontology Learning: Deriving Ontology Schema Components from Unstructured Text. In: Handschuh, S., et al. (eds.) Workshop on Semantic Authoring, Annotation and Knowledge Markup 2007. SEUR-WS, vol. 289., Whistler (2007)Google Scholar
  5. 5.
    Cimiano, P., Hotho, A., Staab, S.: Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis. Journal of Artificial Intelligence Research 24(1), 305–339 (2005)zbMATHGoogle Scholar
  6. 6.
    Wong, W., Liu, W., Bennamoun, M.: Ontology Learning from Text: A Look Back and into the Future. ACM Computing Surveys 44(4), 20 (2012)Google Scholar
  7. 7.
    McDowell, L.K., Cafarella, M.: Ontology-Driven Information Extraction with OntoSyphon. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 428–444. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Khelif, K., Dieng-Kuntz, R., Barbry, P.: An Ontology-based Approach to Support Text Mining and Information Retrieval in the Biological Domain. Journal of Universal Computer Science 13(12), 1881–1907 (2007)Google Scholar
  9. 9.
    Agichtein, E., Gravano, L.: Snowball: Extracting Relations from Large Plain-Text Collections. In: 5th ACM Conference on Digital Libraries, pp. 85–94. ACM Press, New York (2000)Google Scholar
  10. 10.
    Fundel, K., Kuffner, R., Zimmer, R.: RelEx – Relation extraction using dependency parse trees. Bioinformatics 23(3), 365–371 (2007)CrossRefGoogle Scholar
  11. 11.
    Barbero, C., Lombardo, V.: Dependency graphs in natural language processing. In: Gori, M., Soda, G. (eds.) AI*IA 1995. LNCS, vol. 992, pp. 115–126. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  12. 12.
    Lesmo, L., Lombardo, V.: The assignment of grammatical relations in natural language processing. In: 14th Conference on Computational Linguistics. Project Notes with Demonstrations, vol. 4, pp. 1090–1094. Association for Computational Linguistics, Stroudsburg (1992)CrossRefGoogle Scholar
  13. 13.
    Miller, G.A.: WordNet: a lexical database for Engilish. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Da Huang
    • 1
  • Wei Hu
    • 1
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniviersityChina

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