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Automatic Subject Metadata Generation for Scientific Documents Using Wikipedia and Genetic Algorithms

  • Arash Joorabchi
  • Abdulhussain E. Mahdi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7603)

Abstract

Topical annotation of documents with keyphrases is a proven method for revealing the subject of scientific and research documents. However, scientific documents that are manually annotated with keyphrases are in the minority. This paper describes a machine learning-based automatic keyphrase annotation method for scientific documents, which utilizes Wikipedia as a thesaurus for candidate selection from documents’ content and deploys genetic algorithms to learn a model for ranking and filtering the most probable keyphrases. Reported experimental results show that the performance of our method, evaluated in terms of inter-consistency with human annotators, is on a par with that achieved by humans and outperforms rival supervised methods.

Keywords

text mining scientific digital libraries subject metadata keyphrase annotation keyphrase indexing Wikipedia genetic algorithms 

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References

  1. 1.
    Grineva, M., Grinev, M., Lizorkin, D.: Extracting key terms from noisy and multi-theme documents. In: 18th International Conference on World Wide Web, Madrid, Spain (2009)Google Scholar
  2. 2.
    Mahdi, A.E., Joorabchi, A.: A Citation-based approach to automatic topical indexing of scientific literature. Journal of Information Science 36, 798–811 (2010)CrossRefGoogle Scholar
  3. 3.
    Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: practical automatic keyphrase extraction. In: Fourth ACM Conference on Digital Libraries. ACM, Berkeley (1999)Google Scholar
  4. 4.
    Turney, P.D.: Learning Algorithms for Keyphrase Extraction. Inf. Retr. 2, 303–336 (2000)CrossRefGoogle Scholar
  5. 5.
    Turney, P.D.: Coherent keyphrase extraction via web mining. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, Mexico, pp. 434–439 (2003)Google Scholar
  6. 6.
    Nguyen, T.D., Kan, M.-Y.: Keyphrase extraction in scientific publications. In: Proceedings of the 10th International Conference on Asian Digital Libraries, Vietnam, pp. 317–326 (2007)Google Scholar
  7. 7.
    Markó, K.G., Hahn, U., Schulz, S., Daumke, P., Nohama, P.: Interlingual Indexing across Different Languages. In: Computer-Assisted Information Retrieval, RIAO, pp. 82–99 (2004)Google Scholar
  8. 8.
    Pouliquen, B., Steinberger, R., Ignat, C.: Automatic annotation of multilingual text collections with a conceptual thesaurus. Ontologies and Information Extraction. In: Workshop at EUROLAN 2003 (2003)Google Scholar
  9. 9.
    Medelyan, O., Witten, I.H.: Thesaurus based automatic keyphrase indexing. In: Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries, USA, pp. 296–297 (2006)Google Scholar
  10. 10.
    Medelyan, O., Witten, I.H.: Domain-independent automatic keyphrase indexing with small training sets. Journal of the American Society for Information Science and Technology 59, 1026–1040 (2008)CrossRefGoogle Scholar
  11. 11.
    Milne, D., Medelyan, O., Witten, I.H.: Mining Domain-Specific Thesauri from Wikipedia: A Case Study. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 442–448. IEEE Computer Society (2006)Google Scholar
  12. 12.
    Medelyan, O., Milne, D., Legg, C., Witten, I.H.: Mining meaning from Wikipedia. Int. J. Hum.-Comput. Stud. 67, 716–754 (2009)CrossRefGoogle Scholar
  13. 13.
    Medelyan, O., Witten, I.H., Milne, D.: Topic Indexing with Wikipedia. In: First AAAI Workshop on Wikipedia and Artificial Intelligence (WIKIAI 2008). AAAI Press, US (2008)Google Scholar
  14. 14.
    Medelyan, O.: Human-competitive automatic topic indexing. Department of Computer Science. PhD thesis. University of Waikato, New Zealand (2009)Google Scholar
  15. 15.
    Milne, D.: An open-source toolkit for mining Wikipedia. In: New Zealand Computer Science Research Student Conference (2009)Google Scholar
  16. 16.
    Turney, P.D.: Learning to Extract Keyphrases from Text. National Research Council. Institute for Information Technology (1999)Google Scholar
  17. 17.
    Barker, K., Cornacchia, N.: Using Noun Phrase Heads to Extract Document Keyphrases. In: Hamilton, H.J. (ed.) Canadian AI 2000. LNCS (LNAI), vol. 1822, pp. 40–52. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Milne, D., Witten, I.H.: Learning to link with wikipedia. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, USA, pp. 509–518 (2008)Google Scholar
  20. 20.
    Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In: First AAAI Workshop on Wikipedia and Artificial Intelligence (WIKIAI 2008), Chicago, I.L. (2008)Google Scholar
  21. 21.
  22. 22.
    Rolling, L.: Indexing consistency, quality and efficiency. Information Processing & Management 17, 69–76 (1981)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arash Joorabchi
    • 1
  • Abdulhussain E. Mahdi
    • 1
  1. 1.Department of Electronic and Computer EngineeringUniversity of LimerickIreland

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