Terminology-based knowledge mining for new knowledge discovery

ABSTRACT

In this article we present an integrated knowledge-mining system for the domain of biomedicine, in which automatic term recognition, term clustering, information retrieval, and visualization are combined. The primary objective of this system is to facilitate knowledge acquisition from documents and aid knowledge discovery through terminology-based similarity calculation and visualization of automatically structured knowledge. This system also supports the integration of different types of databases and simultaneous retrieval of different types of knowledge. In order to accelerate knowledge discovery, we also propose a visualization method for generating similarity-based knowledge maps. The method is based on real-time terminology-based knowledge clustering and categorization and allows users to observe real-time generated knowledge maps, graphically. Lastly, we discuss experiments using the GENIA corpus to assess the practicality and applicability of the system.

REFERENCES

• 1: Ananiadou, S. and Nenadic, G. 2006. Automatic terminology management in biomedicine. In Text Mining for Biology and Biomedicine, S. Ananiadou and J. McNaught (eds), Artech House, Norwood, MA, Ch.4, 67--98.
• 2: Sophia Ananiadou , Carol Freidman , Jun'ichi Tsujii, Introduction: named entity recognition in biomedicine, Journal of Biomedical Informatics, v.37 n.6, p.393-395, December 2004 [doi>10.1016/j.jbi.2004.08.011]
• 3: Berners-Lee, T. 1998. The Semantic Web as a language of logic. Available at: www.w3.org/DesignIssues/Logic.html.
• 4: Brickle, D. and Guha, R. 2000. Resource description framework (RDF) schema specification 1.0, W3C Candidate Recommendation. Available at: http://www.w3.org/TR/rdf-schema.
• 5: Nigel Collier , Chikashi Nobata , Jun-ichi Tsujii, Extracting the names of genes and gene products with a hidden Markov model, Proceedings of the 18th conference on Computational linguistics, p.201-207, July 31-August 04, 2000, Saarbrücken, Germany [doi>10.3115/990820.990850]
• 6: Frantzi, K., Ananiadou, S., and Mima, H. 2000. Automatic recognition of multi-word terms. Int. J. Digital Libraries 3, 2, 117--132. Special issue.
• 7: Fukuda, K., Tsunoda, T., Tamura, A., and Takagi, T. 1998. Toward information extraction: Identifying protein names from biological papers. In Proceedings of the PSB-98 (Hawaii), 705--716.
• 8: Gaizauskas, R., Demetriou, G., and Humphreys, K. 2000. Term recognition and classification in biological science journal articles. In Proceedings of the Workshop on Computational Terminology for Medical and Biological Applications (NLP-2000, Patras, Greece), 37--44.
• 9: Gamper, J., Nejdl, W., and Wolpers, M. 1999. Combining ontologies and terminologies in information systems. In Proceedings of the 5th International Congress on Terminology and Knowledge Engineering, (Innsbruck, Austria), 152--168.
• 10: Genia Project. 2002. Genia project home page. www-tsujii.is.s.u-tokyo.ac.jp/GENIA/.
• 11: Hatzivassiloglou, V., Duboue, P., and Rzhetsky, A. 2001. Disambiguating proteins, genes, and RNA in text: A machine learning approach. Bioinformatics 17, 1, S97--S106.
• 12: Jacquemin, C. 2001. Spotting and Discovering Terms through NLP. MIT Press, Cambridge, MA, 378.
• 13: Krauthammer, M., Rzhetsky, A., Morozov, P., and Friedman, C. 2000. Using BLAST for identifying gene and protein names in journal articles. Gene 259, 245--252.
• 14: Michael Krauthammer , Goran Nenadic, Term identification in the biomedical literature, Journal of Biomedical Informatics, v.37 n.6, p.512-526, December 2004 [doi>10.1016/j.jbi.2004.08.004]
• 15: Medline (National Library of Medicine). 2002. http://www.ncbi.nlm.nih.gov/PubMed/.
• 16: Hideki Mima , Sophia Ananiadou , Goran Nenadic, The ATRACT Workbench: Automatic Term Recognition and Clustering for Terms, Proceedings of the 4th International Conference on Text, Speech and Dialogue, p.126-133, September 11-13, 2001
• 17: Mima, H. and Ananiadou, S. 2001b. An application and evaluation of the C/NC-value approach for the automatic term recognition of multi-word units in Japanese. Int. J. Terminology 6/2, 175--194.
• 18: Nenadic, G., Ananiadou, S., and McNaught, J. 2004. Enhancing automatic term recognition through term variation, In Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004, Geneva, Switzerland).
• 19: Irena Spasic , Sophia Ananiadou , Junichi Tsujii, MaSTerClass: a case-based reasoning system for the classification of biomedical terms, Bioinformatics, v.21 n.11, p.2748-2758, June 2005 [doi>10.1093/bioinformatics/bti338]
• 20: Spasic, I., Ananiadou, S., McNaught, J., and Kumar, A. 2005b. Text mining and ontologies in biomedicine: Making sense of raw text. Briefings in Bioinformatics 6, 3, 239--251.
• 21: TinySVM. 2004. http://chasen.org/~taku/software/TinySVM/.
• 22: UMLS. 2004. http://www.nlm.nih.gov/research/umls/.
• 23: Akira Ushioda, Hierarchical clustering of words, Proceedings of the 16th conference on Computational linguistics, August 05-09, 1996, Copenhagen, Denmark [doi>10.3115/993268.993390]
• 24: Visser, P. R. S., Jones, D. M., Bench-Capon, T. J. M., and Shave, M. J. R. 1997. An analysis of ontology mismatches---Heterogeneity versus interoperability. In Proceedings of the AAAI 1997 Spring Symposium on Ontological Engineering (Stanford University, Stanford, CA), 164--172.
• 25: Voutilainen, A. and Heikkila, J. 1993. An English constraint grammar (ENGCG), a surface-syntactic parser of English. In Creating and Using English Language Corpora, U. Fries et al. (eds.), Rodopi, Amsterdam, 189--199.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Linguistic processing
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)
          Subjects: Natural language

  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING
      I.7.5 Document Capture
          Subjects: Document analysis

General Terms:
Algorithms, Design, Experimentation

Keywords:
Automatic term recognition, biomedicine, natural language processing, structuring knowledge, terminology, visualization