ConNeKTion: A Tool for Handling Conceptual Graphs Automatically Extracted from Text

  • Fabio Leuzzi
  • Stefano Ferilli
  • Fulvio Rotella
Part of the Communications in Computer and Information Science book series (CCIS, volume 385)


Studying, understanding and exploiting the content of a digital library, and extracting useful information thereof, require automatic techniques that can effectively support the users. To this aim, a relevant role can be played by concept taxonomies. Unfortunately, the availability of such a kind of resources is limited, and their manual building and maintenance are costly and error-prone. This work presents ConNeKTion, a tool for conceptual graph learning and exploitation. It allows to learn conceptual graphs from plain text and to enrich them by finding concept generalizations. The resulting graph can be used for several purposes: finding relationships between concepts (if any), filtering the concepts from a particular perspective, extracting keyword, retrieving information and identifying the author. ConNeKTion provides also a suitable control panel, to comfortably carry out these activities.


Digital Library Vector Space Model Plain Text Word Sense Disambiguation Formal Concept Analysis 
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.
    Argamon, S., Saric, M., Stein, S.S.: Style mining of electronic messages for multiple authorship discrimination: first results. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) KDD 2003, pp. 475–480. ACM (2003)Google Scholar
  2. 2.
    Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. J. Artif. Int. Res. 24(1), 305–339 (2005)zbMATHGoogle Scholar
  3. 3.
    de Marneffe, M.C., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure trees. In: LREC (2006)Google Scholar
  4. 4.
    Deerwester, S.: Improving Information Retrieval with Latent Semantic Indexing. In: Borgman, C.L., Pai, E.Y.H. (eds.) Proceedings of the 51st ASIS Annual Meeting (ASIS 1988), vol. 25. American Society for Information Science, Atlanta (October 1988)Google Scholar
  5. 5.
    Dhillon, I.S., Modha, D.S.: Concept decompositions for large sparse text data using clustering. In: Machine Learning, pp. 143–175 (2001)Google Scholar
  6. 6.
    Diederich, J., Kindermann, J., Leopold, E., Paass, G.: Authorship attribution with support vector machines. Applied Intelligence 19(1-2), 109–123 (2003)CrossRefzbMATHGoogle Scholar
  7. 7.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  8. 8.
    Ferilli, S., Biba, M., Basile, T.M., Esposito, F.: Combining qualitative and quantitative keyword extraction methods with document layout analysis. In: Post-proceedings of the 5th Italian Research Conference on Digital Library Management Systems (IRCDL 2009), pp. 22–33 (2009)Google Scholar
  9. 9.
    Ferilli, S., Biba, M., Di Mauro, N., Basile, T.M.A., Esposito, F.: Plugging taxonomic similarity in first-order logic horn clauses comparison. In: Serra, R., Cucchiara, R. (eds.) AI*IA 2009. LNCS, vol. 5883, pp. 131–140. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Ferilli, S., Leuzzi, F., Rotella, F.: Cooperating techniques for extracting conceptual taxonomies from text. In: Proceedings of the Workshop on MCP at AI*IA XIIth Conference (2011)Google Scholar
  11. 11.
    Hamming, R.W.: Error detecting and error correcting codes. Bell System Technical Journal 29(2), 147–160 (1950)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Jay, R., Jay, A.: Effective Presentation: How to Create and Deliver a Winning Presentation. Prentice Hall (2004)Google Scholar
  13. 13.
    Jones, W.P., Furnas, G.W.: Pictures of relevance: A geometric analysis of similarity measures. Journal of the American Society for Information Science 38(6), 420–442 (1987)CrossRefGoogle Scholar
  14. 14.
    Karypis, G., Han, E.-H.(S.): Concept indexing: A fast dimensionality reduction algorithm with applications to document retrieval and categorization. Technical report, IN CIKM 2000 (2000)Google Scholar
  15. 15.
    Klein, D., Manning, C.D.: Fast exact inference with a factored model for natural language parsing. In: Advances in Neural Information Processing Systems, vol. 15. MIT Press (2003)Google Scholar
  16. 16.
    Leuzzi, F., Ferilli, S., Rotella, F.: Improving robustness and flexibility of concept taxonomy learning from text. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) NFMCP 2012 Workshop. LNCS (LNAI), vol. 7765, pp. 170–184. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  17. 17.
    Maedche, A., Staab, S.: Mining ontologies from text. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNCS (LNAI), vol. 1937, pp. 189–202. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  18. 18.
    Maedche, A., Staab, S.: The text-to-onto ontology learning environment. In: ICCS-2000 - Eight ICCS, Software Demonstration (2000)Google Scholar
  19. 19.
    Matsuo, Y., Ishizuka, M.: Keyword extraction from a single document using word co-occurrence statistical information. International Journal on Artificial Intelligence Tools 13, 2004 (2003)Google Scholar
  20. 20.
    Ogata, N.: A formal ontology discovery from web documents. In: Zhong, N., Yao, Y., Ohsuga, S., Liu, J. (eds.) WI 2001. LNCS (LNAI), vol. 2198, pp. 514–519. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  21. 21.
    Cucchiarelli, A., Velardi, P., Navigli, R., Neri, F.: Evaluation of OntoLearn, a methodology for automatic population of domain ontologies. In: Ontology Learning from Text: Methods, Applications and Evaluation. IOS Press (2006)Google Scholar
  22. 22.
    De Raedt, L., Kimmig, A., Toivonen, H.: Problog: a probabilistic prolog and its application in link discovery. In: Proceedings of 20th IJCAI, pp. 2468–2473. AAAI Press (2007)Google Scholar
  23. 23.
    Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M.M., Gatford, M.: Okapi at trec-3, pp. 109–126 (1996)Google Scholar
  24. 24.
    Rotella, F., Ferilli, S., Leuzzi, F.: A domain based approach to information retrieval in digital libraries. In: Agosti, M., Esposito, F., Ferilli, S., Ferro, N. (eds.) IRCDL 2012. CCIS, vol. 354, pp. 129–140. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  25. 25.
    Salton, G.: The SMART Retrieval System—Experiments in Automatic Document Processing. Prentice-Hall, Inc., Upper Saddle River (1971)Google Scholar
  26. 26.
    Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company (1984)Google Scholar
  27. 27.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18, 613–620 (1975)CrossRefzbMATHGoogle Scholar
  28. 28.
    Sato, T.: A statistical learning method for logic programs with distribution semantics. In: Proceedings of the 12th ICLP 1995, pp. 715–729. MIT Press (1995)Google Scholar
  29. 29.
    Singhal, A., Buckley, C., Mitra, M., Mitra, A.: Pivoted document length normalization, pp. 21–29. ACM Press (1996)Google Scholar
  30. 30.
    Tweedie, F.J., Singh, S., Holmes, D.I.: Neural network applications in stylometry: The federalist papers. Computers and the Humanities 30(1), 1–10 (1996)CrossRefGoogle Scholar
  31. 31.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on ACL, pp. 133–138. ACL, Morristown (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Fabio Leuzzi
    • 1
  • Stefano Ferilli
    • 1
    • 2
  • Fulvio Rotella
    • 1
  1. 1.Dipartimento di InformaticaUniversità di BariItaly
  2. 2.Centro Interdipartimentale per la Logica e sue ApplicazioniUniversità di BariItaly

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