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SCMS – Semantifying Content Management Systems

  • Axel-Cyrille Ngonga Ngomo
  • Norman Heino
  • Klaus Lyko
  • René Speck
  • Martin Kaltenböck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7032)

Abstract

The migration to the Semantic Web requires from CMS that they integrate human- and machine-readable data to support their seamless integration into the Semantic Web. Yet, there is still a blatant need for frameworks that can be easily integrated into CMS and allow to transform their content into machine-readable knowledge with high accuracy. In this paper, we describe the SCMS (Semantic Content Management Systems) framework, whose main goals are the extraction of knowledge from unstructured data in any CMS and the integration of the extracted knowledge into the same CMS. Our framework integrates a highly accurate knowledge extraction pipeline. In addition, it relies on the RDF and HTTP standards for communication and can thus be integrated in virtually any CMS. We present how our framework is being used in the energy sector. We also evaluate our approach and show that our framework outperforms even commercial software by reaching up to 96% F-score.

Keywords

Commercial System Name Entity Recognition Computational Linguistics Entity Recognition Relation Extraction 
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.

References

  1. 1.
    Adrian, B., Hees, J., Herman, I., Sintek, M., Dengel, A.: Epiphany: Adaptable rDFa Generation Linking the Web of Documents to the Web of Data. In: Cimiano, P., Pinto, H.S. (eds.) EKAW 2010. LNCS, vol. 6317, pp. 178–192. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Agichtein, E., Gravano, L.: Snowball: Extracting relations from large plain-text collections. In: ACM DL, pp. 85–94 (2000)Google Scholar
  3. 3.
    Amsler, R.: Research towards the development of a lexical knowledge base for natural language processing. SIGIR Forum 23, 1–2 (1989)CrossRefGoogle Scholar
  4. 4.
    Auer, S., Dietzold, S., Riechert, T.: OntoWiki – A Tool for Social, Semantic Collaboration. 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. 736–749. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Brin, S.: Extracting Patterns and Relations from the World Wide Web. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 172–183. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Coates-Stephens, S.: The analysis and acquisition of proper names for the understanding of free text. Computers and the Humanities 26, 441–456 (1992) 10.1007/BF00136985CrossRefGoogle Scholar
  7. 7.
    Curran, J.R., Clark, S.: Language independent ner using a maximum entropy tagger. In: HLT-NAACL, pp. 164–167 (2003)Google Scholar
  8. 8.
    Dietterich, T.G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Etzioni, O., Cafarella, M., Downey, D., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165, 91–134 (2005)CrossRefGoogle Scholar
  10. 10.
    Finkel, J., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: ACL, pp. 363–370 (2005)Google Scholar
  11. 11.
    Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C., Nevill-Manning, C.G.: Domain-specific keyphrase extraction. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 1999, pp. 668–673. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  12. 12.
    Grishman, R., Yangarber, R.: Nyu: Description of the Proteus/Pet system as used for MUC-7 ST. In: MUC-7. Morgan Kaufmann (1998)Google Scholar
  13. 13.
    Harabagiu, S., Bejan, C.A., Morarescu, P.: Shallow semantics for relation extraction. In: IJCAI, pp. 1061–1066 (2005)Google Scholar
  14. 14.
    Huynh, D., Mazzocchi, S., Karger, D.R.: Piggy Bank: Experience the Semantic Web Inside Your Web Browser. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 413–430. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Kim, S.N., Kan, M.-Y.: Re-examining automatic keyphrase extraction approaches in scientific articles. In: MWE 2009, pp. 9–16 (2009)Google Scholar
  16. 16.
    Kim, S.N., Medelyan, O., Kan, M.-Y., Baldwin, T.: Semeval-2010 task 5: Automatic keyphrase extraction from scientific articles. In: SemEval 2010, pp. 21–26. Association for Computational Linguistics, Stroudsburg (2010)Google Scholar
  17. 17.
    Matsuo, Y., Ishizuka, M.: Keyword Extraction From A Single Document Using Word Co-Occurrence Statistical Information. International Journal on Artificial Intelligence Tools 13(1), 157–169 (2004)CrossRefGoogle Scholar
  18. 18.
    Nadeau, D.: Semi-Supervised Named Entity Recognition: Learning to Recognize 100 Entity Types with Little Supervision. PhD thesis, University of Ottawa (2007)Google Scholar
  19. 19.
    Nadeau, D., Turney, P., Matwin, S.: Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity. In: Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006. LNCS (LNAI), vol. 4013, pp. 266–277. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Nguyen, D.P.T., Matsuo, Y., Ishizuka, M.: Relation extraction from wikipedia using subtree mining. In: AAAI, pp. 1414–1420 (2007)Google Scholar
  21. 21.
    Nguyen, T.D., Kan, M.-Y.: Keyphrase Extraction in Scientific Publications. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 317–326. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Pantel, P., Pennacchiotti, M.: Espresso: Leveraging generic patterns for automatically harvesting semantic relations. In: ACL, pp. 113–120 (2006)Google Scholar
  23. 23.
    Park, Y., Byrd, R.J., Boguraev, B.K.: Automatic glossary extraction: beyond terminology identification. In: COLING, pp. 1–7 (2002)Google Scholar
  24. 24.
    Pasca, M., Lin, D., Bigham, J., Lifchits, A., Jain, A.: Organizing and searching the world wide web of facts - step one: the one-million fact extraction challenge. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 2, pp. 1400–1405. AAAI Press (2006)Google Scholar
  25. 25.
    Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: CONLL, pp. 147–155 (2009)Google Scholar
  26. 26.
    Thielen, C.: An approach to proper name tagging for german. In: Proceedings of the EACL 1995 SIGDAT Workshop (1995)Google Scholar
  27. 27.
    Tramp, S., Heino, N., Auer, S., Frischmuth, P.: RDFauthor: Employing RDFa for Collaborative Knowledge Engineering. In: Cimiano, P., Pinto, H.S. (eds.) EKAW 2010. LNCS, vol. 6317, pp. 90–104. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Turney, P.D.: Coherent keyphrase extraction via web mining. In: IJCAI, San Francisco, CA, USA, pp. 434–439 (2003)Google Scholar
  29. 29.
    Walker, D., Amsler, R.: The use of machine-readable dictionaries in sublanguage analysis. Analysing Language in Restricted Domains (1986)Google Scholar
  30. 30.
    Wang, G., Yu, Y., Zhu, H.: PORE: Positive-Only Relation Extraction from Wikipedia Text. 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. 580–594. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  31. 31.
    Yan, Y., Okazaki, N., Matsuo, Y., Yang, Z., Ishizuka, M.: Unsupervised relation extraction by mining wikipedia texts using information from the web. In: ACL 2009, pp. 1021–1029 (2009)Google Scholar
  32. 32.
    Zhou, G., Su, J.: Named entity recognition using an hmm-based chunk tagger. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL 2002, pp. 473–480. Association for Computational Linguistics, Morristown (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Axel-Cyrille Ngonga Ngomo
    • 1
  • Norman Heino
    • 1
  • Klaus Lyko
    • 1
  • René Speck
    • 1
  • Martin Kaltenböck
    • 2
  1. 1.AKSW GroupUniversity of LeipzigLeipzigGermany
  2. 2.Semantic Web CompanyViennaAustria

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