Skip to main content
SpringerLink
Log in

Metamodel of Ontology Learning from Text

  • Chapter
Book cover Emergent Web Intelligence: Advanced Semantic Technologies

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

Ontologies play a pervasive role in many areas of IT. Over the last decade a substantial number of ontologies have been developed. However, while looking for a specific ontology it is difficult to find the right one because of the problems of the ontology unavailability or inadequacy. Although many ontology learning methods already exist, there are no comprehensive models of the whole process of the ontology learning from text. In this article, the metamodel of the ontology learning from text is presented. The approach is based on the survey of the existing methods, while evaluation is provided in the form of a reference implementation of the introduced metamodel.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.w3.org/RDF/.

  2. 2.

    http://www.w3.org/2004/OWL/.

  3. 3.

    http://www.w3.org/.

  4. 4.

    http://www.schemaweb.info.

  5. 5.

    http://www.daml.org/ontologies/.

  6. 6.

    http://swoogle.umbc.edu/.

  7. 7.

    Last updated: 4.04.2008.

  8. 8.

    Last updated: 4.04.2008.

  9. 9.

    Last updated: 4.04.2008.

  10. 10.

    http://lcl.di.uniroma1.it/people.jsp. Last accessed: 15.05.2008.

  11. 11.

    http://www.omg.org/mof/.

  12. 12.

    Some authors prefer to exclude an instance extraction from a concept extraction task from historical divisions into terminological (TBox) and assertional knowledge (ABox). Still, the methods used for these two tasks in the ontology learning are similar.

  13. 13.

    http://www.w3.org/Submission/SWRL/.

  14. 14.

    http://znak.pl.

  15. 15.

    We had the pleasure to welcome a KMi researcher from Open University as a guest at our university.

References

  1. Abramowicz, W., Vargas-Vera, M., Wisniewski, M.: Axiom-based feedback cycle for relation extraction in ontology learning from text. In: DEXA ’08: Proceedings of the 19th International Conference on Database and Expert Systems Applications. IEEE Comput. Soc., Los Alamitos (2008)

    Google Scholar 

  2. Abramowicz, W., Wisniewski, M.: Proximity window context method for term extraction in ontology learning from text. In: DEXA ’08: Proceedings of the 19th International Conference on Database and Expert Systems Applications. IEEE Comput. Soc., Los Alamitos (2008)

    Google Scholar 

  3. Adar, E.: Sarad: A simple and robust abbreviation dictionary. Bioinformatics 20(4), 527–533 (2004)

    Article  Google Scholar 

  4. Agirre, E., Ansa, O., Hovy, E., Martínez, D.: Enriching very large ontologies using the www. In: Proc. of the Ontology Learning Workshop, ECAI, Berlin, Germany (2000)

    Google Scholar 

  5. Alfonseca, E., Manandhar, S.: Extending a lexical ontology by a combination of distributional semantics signatures. In: Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2002) (2002)

    Google Scholar 

  6. Alfonseca, E., Manandhar, S.: Improving an ontology refinement method with hyponymy patterns. In: Language Resources and Evaluation (LREC-2002), Las Palmas, Spain (2002)

    Google Scholar 

  7. Aussenac-Gilles, N., Biébow, B., Szulman, S.: Revisiting ontology design: A methodology based on corpus analysis. In: Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management. Springer, Berlin (2000)

    Google Scholar 

  8. Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  9. Basili, R., Pazienza, M.T., Velardi, P.: An empirical symbolic approach to natural language processing. Artificial Intelligence 85, 59–99 (1996)

    Article  Google Scholar 

  10. Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284(5) (2001)

    Google Scholar 

  11. Brody, S., Navigli, R., Lapata, M.: Ensemble methods for unsupervised WSD. In: ACL ’06: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL, pp. 97–104, Association for Computational Linguistics, Morristown, NJ, USA (2006)

    Google Scholar 

  12. Budanitsky, A., Hirst, G.: Evaluating WordNet-based measures of lexical semantic relatedness. Computational Linguistics 32(1), 13–47 (2006)

    Article  MATH  Google Scholar 

  13. Buitelaar, P., Cimiano, P.: Ontology learning from text: Tutorial. In: 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy (2006)

    Google Scholar 

  14. Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: An overview. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Evaluation and Applications. Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam (2005)

    Google Scholar 

  15. Buitelaar, P., Olejnik, D., Sintek, M.: A protege plug-in for ontology extraction from text based on linguistic analysis. In: Proceedings of the 1st European Semantic Web Symposium (ESWS) (2004)

    Google Scholar 

  16. Buitelaar, P., Sintek, M.: Ontolt version 1.0: Middleware for ontology extraction from text. In: Proceedings of the Demo Session at the International Semantic Web Conference (ISWC) (2004)

    Google Scholar 

  17. Bunescu, R., Mooney, R.: Learning to extract relations from the web using minimal supervision. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Association for Computational Linguistics, Prague, Czech Republic (June 2007)

    Google Scholar 

  18. Caraballo, S.A.: Automatic construction of a hypernym-labeled noun hierarchy from text. In: Proceedings of the Conference of the Association for Computational Linguistics (1999)

    Google Scholar 

  19. Caraballo, S.A.: Automatic construction of a hypernym—labeled noun hierarchy from text. PhD thesis, Providence, RI, USA, 2001. Adviser—E. Charniak

    Google Scholar 

  20. Cederberg, S., Widdows, D.: Using LSA and noun coordination information to improve the precision and recall of automatic hyponymy extraction. In: Proceedings of the Conference on Natural Language Learning (CoNNL) (2003)

    Google Scholar 

  21. Chang, J., Schutze, H.: Abbreviations in biomedical text. In: Ananiadou, S., Mcnaught, J. (eds.) Text Mining for Biology and Biomedicine, pp. 99–119. Artech House, Norwood (2006)

    Google Scholar 

  22. Charniak, E., Berland, M.: Finding parts in very large corpora. In: Proceedings of the 37th Annual Meeting of the ACL (1999)

    Google Scholar 

  23. Cimiano, P.: Ontology learning from text. PhD thesis, University of Karlsruhe (2006)

    Google Scholar 

  24. Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. Journal of Artificial Intelligence Research 24, 305–339 (2005)

    MATH  Google Scholar 

  25. Cimiano, P., Schmidt-Thieme, L., Pivk, A., Staab, S.: Learning taxonomic relations from heterogeneous evidence. In: Ontology Learning from Text: Methods, Applications and Evaluation, pp. 59–73. IOS Press, Amsterdam (2005)

    Google Scholar 

  26. Cimiano, P., Staab, S.: Learning concept hierarchies from text with a guided agglomerative clustering algorithm. In: ICML 2005 Workshop on Learning and Extending Lexical Ontologies with Machine Learning Methods (2005)

    Google Scholar 

  27. Cimiano, P., Völker, J.: Text2onto—a framework for ontology learning and data-driven change discovery. In: 10th International Conference on Applications of Natural Language to Information Systems (NLDB’2005) (2005)

    Google Scholar 

  28. Cimiano, P., Wenderoth, J.: Automatically learning qualia structures from the web. In: Proceedings of the ACL Workshop on Deep Lexical Acquisition (2005)

    Google Scholar 

  29. Cristani, M., Cuel, R.: A survey on ontology creation methodologies. International Journal on Semantic Web and Information Systems 1(2), 49–69 (2005)

    Article  Google Scholar 

  30. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: Gate: A framework and graphical development environment for robust NLP tools and applications. In: Proceedings of the 40th Annual Meeting of the ACL (2002)

    Google Scholar 

  31. Cunningham, H., Maynard, D., Tablan, V.: Jape: A java annotation patterns engine (2nd edn.). Technical report, Department of Computer Science, University of Sheffield (November 2000)

    Google Scholar 

  32. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V., Ursu, C., Dimitrov, M., Dowman, M., Aswani, N., Roberts, I., Li, Y., Shafirin, A.: Developing Language Processing Components with GATE Version 4. Department of Computer Science, University of Sheffield, 4.0-beta1 edition, April 2007

    Google Scholar 

  33. Daille, B.: Study and implementation of combined techniques for automatic extraction of terminology. In: Klavans, J., Resnik, P. (eds.) The Balancing Act: Combining Symbolic and Statistical Approaches to Language, pp. 49–66. MIT Press, Cambridge (1996)

    Google Scholar 

  34. Dunning, T.: Accurate methods for the statistics of surprise and coincidence. Computational Linguistics 19(1), 61–74 (1993)

    Google Scholar 

  35. Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  36. Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer, New York (2003)

    Google Scholar 

  37. Fensel, D., van Harmelen, F., Klein, M., Akkermans, H., Broekstra, J., Fluit, C., van der Meer, J., Schnurr, H.-P., Studer, R., Hughes, J., Krohn, U., Davies, J., Engels, R., Bremdal, B., Ygge, F., Lau, T., Novotny, B., Reimer, U., Horrocks, I.: Onto-knowledge: Ontology-based tools for knowledge management. In: Proceedings of the eBusiness and eWork 2000 (eBeW’00) Conference, Madrid, Spain (2000)

    Google Scholar 

  38. Fotzo, H.N., Gallinari, P.: Learning generalization/specialization relations between concepts—application for automatically building thematic document hierarchies. In: RIAO (2004)

    Google Scholar 

  39. Frantzi, K., Ananiadou, S., Mima, H.: Automatic recognition of multi-word terms: The C-value/NC-value method. International Journal on Digital Libraries 3(2), 115–130 (2000)

    Article  Google Scholar 

  40. Girju, R., Moldovan, D.: Text mining for causal relations. In: Proceedings of the FLAIRS Conference (2002)

    Google Scholar 

  41. Grefenstette, G.: Cross-Language Information Retrieval. Kluwer International Series on Information Retrieval. Kluwer Academic, Boston (1998)

    Book  Google Scholar 

  42. Gruber, T.R.: A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220 (1993)

    Article  Google Scholar 

  43. Gruber, T.: Ontology. In: Liu, L., Tamer Ozsu, M. (eds.) Encyclopedia of Database Systems. Springer, Berlin (2008)

    Google Scholar 

  44. Haase, P., Völker, J.: Ontology learning and reasoning—dealing with uncertainty and inconsistency. In: Proceedings of the Workshop on Uncertainty Reasoning for the Semantic Web (URSW) (2005)

    Google Scholar 

  45. Hammerton, J., Osborne, M., Armstrong, S., Daelemans, W.: Introduction to special issue on machine learning approaches to shallow parsing. Journal of Machine Learning Research 2002(2), 8 (2002)

    Google Scholar 

  46. Hamp, B., Feldweg, H.: Germanet—a lexical-semantic net for German. In: Proceedings of ACL Workshop Automatic Information Extraction and Building of Lexical Semantic Resources for NLP Applications, Madrid, Spain (1997)

    Google Scholar 

  47. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: 14th International Conference on Computational Linguistics (1992)

    Google Scholar 

  48. Hearst, M.A.: Automated discovery of WordNet relations. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database and Some of its Applications, pp. 132–152. MIT Press, Cambridge (1998)

    Google Scholar 

  49. Hepp, M.: Products and services ontologies: A methodology for deriving owl ontologies from industrial categorization standards. International Journal on Semantic Web and Information Systems 2(1), 72–99 (2006)

    Article  Google Scholar 

  50. Hepp, M.: Ontologies: State of the art, business potential, and grand challenges. In: Ontology Management, pp. 3–22. Springer, Berlin (2008)

    Chapter  Google Scholar 

  51. Hepp, M., De Leenheer, P., de Moor, A., Sure, Y.: Ontology Management, Semantic Web, Semantic Web Services, and Business Applications. Semantic Web and Beyond Computing for Human Experience, vol. 7. Springer, Berlin (2008)

    Google Scholar 

  52. Hepple, M.: Independence and commitment: Assumptions for rapid training and execution of rule-based POS taggers. In: Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL-2000), Hong Kong (2000)

    Google Scholar 

  53. Huang, J.-X., Shin, J.-A., Choi, K.-S.: Integrating relations for a domain ontology. In: Proceedings of the 6th International Semantic Web Conference, Busan, Korea (November 2007)

    Google Scholar 

  54. International Organization for Standardization. ISO 1087-1:2000 Terminology Work—Vocabulary—Part 1: Theory and Application (2000)

    Google Scholar 

  55. International Organization for Standardization. ISO 704:2000 Terminology Work—Principles and Methods (2000)

    Google Scholar 

  56. International Organization for Standardization. ISO 860:2007 Terminology Work—Harmonization of Concepts and Terms (2007)

    Google Scholar 

  57. Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice-Hall, Upper Saddle River (2000)

    Google Scholar 

  58. Kauffman, R.J., Walden, E.A.: Economics and electronic commerce: Survey and directions for research. International Journal of Electronic Commerce 5(4), 5–116 (2001)

    Google Scholar 

  59. Kenter, T., Maynard., D.: Using GATE as an annotation tool. Department of Computer Science, University of Sheffield (January 2005)

    Google Scholar 

  60. Kietz, J., Maedche, A., Volz, R.: A method for semi-automatic ontology acquisition from a corporate intranet. In: Workshop “Ontologies and Text”, co-located with EKAW’2000 (2000)

    Google Scholar 

  61. Kipfer, B.A.: Roget new millennium thesaurus, 1st edn. (v 1.1.1), 2006-04-03 (2006)

    Google Scholar 

  62. Lin, D., Pantel, P.: Dirt—discovery of inference rules from text. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2001)

    Google Scholar 

  63. Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic, Boston (2002)

    Book  MATH  Google Scholar 

  64. Maedche, A., Staab, S.: Discovering conceptual relations from text. In: ECAI 2000. Proceedings of the 14th European Conference on Artificial Intelligence, Berlin, Germany. IOS Press, Amsterdam (2000)

    Google Scholar 

  65. Maedche, A., Staab, S.: Semi-automatic engineering of ontologies from text. In: Proceedings of the 12th International Conference on Software Engineering and Knowledge Engineering (2000)

    Google Scholar 

  66. Maedche, A., Staab, S.: The text-to-onto ontology learning environment. In: Proceedings of the 12th Internal Conference on Software and Knowledge Engineering, Chicago, USA (2000)

    Google Scholar 

  67. Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  68. Medjahed, B., Benatallah, B., Bouguettaya, A., Ngu, A.H.H., Elmagarmid, A.K.: Business-to-business interactions: Issues and enabling technologies. The VLDB Journal 12(1), 59–85 (2003)

    Article  Google Scholar 

  69. Missikoff, M., Navigli, R., Velardi, P.: Integrated approach to web ontology learning and engineering. IEEE Computer 35(11), 60–63 (2002)

    Article  Google Scholar 

  70. Nadeau, D.: Balie—baseline information extraction. Multilingual information extraction from text with machine learning and natural language techniques. Technical report, School of Information Technology and Engineering, University of Ottawa, Canada (2005)

    Google Scholar 

  71. Narayanan, S., Petruck, M.R.L., Baker, C.F., Fillmore, C.J.: Putting FrameNet data into the ISO linguistic annotation framework. In: Proceedings of the ACL 2003 Workshop on Linguistic Annotation, pp. 22–29, Association for Computational Linguistics, Morristown, NJ, USA (2003)

    Google Scholar 

  72. Navigli, R.: Meaningful clustering of senses helps boost word sense disambiguation performance. In: ACL ’06: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL, pp. 105–112, Association for Computational Linguistics, Morristown, NJ, USA (2006)

    Google Scholar 

  73. Navigli, R., Velardi, P.: Learning domain ontologies from document warehouses and dedicated web sites. Computational Linguistics 30(2), 151–179 (2004)

    Article  MATH  Google Scholar 

  74. Navigli, R., Velardi, P.: Structural semantic interconnections: A knowledge-based approach to word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(7), 1075–1086 (2005)

    Article  Google Scholar 

  75. Navigli, R., Velardi, P., Cucchiarelli, A., Neri, F.: Quantitative and qualitative evaluation of the OntoLearn ontology learning system. In: COLING ’04: Proceedings of the 20th International Conference on Computational Linguistics, p. 1043, Association for Computational Linguistics, Morristown, NJ, USA (2004)

    Google Scholar 

  76. Nenadic, G., Ananiadou, S., McNaught, J.: Enhancing automatic term recognition through recognition of variation. In: COLING ’04: Proceedings of the 20th International Conference on Computational Linguistics, p. 604, Association for Computational Linguistics, Morristown, NJ, USA (2004)

    Google Scholar 

  77. Niles, I., Pease, A.: Towards a standard upper ontology. In: FOIS ’01: Proceedings of the International Conference on Formal Ontology in Information Systems. ACM, New York (2001)

    Google Scholar 

  78. Ogden, C.K., Richards, I.A.: The Meaning of Meaning: A Study of the Influence of Language upon Thought and of the Science of Symbolism. International Library of Psychology, Philosophy, and Scientific Method. Harcourt Brace, New York (1923)

    Google Scholar 

  79. Okazaki, N., Ananiadou, S.: A term recognition approach to acronym recognition. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, Association for Computational Linguistics, Morristown, NJ, USA (2006)

    Google Scholar 

  80. Piasecki, M., Broda, B.: Semantic similarity measure of Polish nouns based on linguistic features. In: Proceedings of 10th International Conference on Business Information Systems, Poznan, Poland. Lecture Notes in Computer Science. Springer, Berlin (2007)

    Google Scholar 

  81. Pinto, H.S., Martins, J.P.: Ontologies: How can they be built? Knowledge and Information Systems 6(4), 441–464 (2004)

    Article  Google Scholar 

  82. Piskorski, J., Drozdzynski, W., Krieger, H.-U., Schafer, U.: Sprout—a general-purpose NLP framework integrating finite-state and unification-based grammar formalisms. In: Proceedings of the 5th International Workshop on Finite-State Methods and Natural Language Processing, Helsinki, Finland. Lecture Notes in Artificial Intelligence. Springer, Berlin (2005)

    Google Scholar 

  83. Poesio, M., Almuhareb, A.: Identifying concept attributes using a classifier. In: Proceedings of the ACL Workshop on Deep Lexical Acquisition (2005)

    Google Scholar 

  84. Poesio, M., Ishikawa, T., Schulte im Walde, S., Vieira, R.: Acquiring lexical knowledge for anaphora resolution. In: Proceedings of the 3rd Conference on Language Resources and Evaluation (2002)

    Google Scholar 

  85. Rinaldi, F., Yuste, E.: Exploiting technical terminology for knowledge management. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Evaluation and Applications. Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam (2005)

    Google Scholar 

  86. Roux, C., Proux, D., Rechenmann, F., Julliard, L.: An ontology enrichment method for a pragmatic information extraction system gathering data on genetic interactions. In: Proceedings of the ECAI2000 Workshop on Ontology Learning (OL2000), Berlin, Germany (2000)

    Google Scholar 

  87. Sanderson, M., Croft, B.: Deriving concept hierarchies from text. In: SIGIR ’99. ACM, New York (1999)

    Google Scholar 

  88. Schwartz, A., Hearst, M.: A simple algorithm for identifying abbreviation definitions in biomedical texts. In: Proceedings of the Pacific Symposium on Biocomputing PSB 2003 (2003)

    Google Scholar 

  89. Simperl, E.P.B., Sure, Y., Tempich, C.: Ontocom: A cost estimation model for ontology engineering. In: Proceedings of the 5th International Semantic Web Conference, Athens, Georgia (November 2006)

    Google Scholar 

  90. Singh, R., Iyer, L.S., Salam, A.F.: Semantic ebusiness. International Journal on Semantic Web and Information Systems 1(1), 19–35 (2005)

    Article  Google Scholar 

  91. Sintek, M., Buitelaar, P., Olejnik, D.: A formalization of ontology learning from text. In: International Semantic Web Conference. Hiroshima, Japan (2004)

    Google Scholar 

  92. Smadja, F.: Retrieving collocations from text: Xtract. Computational Linguistics 19(1), 143–177 (1993)

    Google Scholar 

  93. Snow, R., Jurafsky, D., Ng, A.Y.: Semantic taxonomy induction from heterogeneous evidence. In: ACL ’06: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL, pp. 801–808, Association for Computational Linguistics, Morristown, NJ, USA (2006)

    Google Scholar 

  94. Sowa, J.F.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)

    MATH  Google Scholar 

  95. Sowa, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole, Pacific Grove (2000)

    Google Scholar 

  96. Sowa, J.F.: Ontology, metadata, and semiotics. In: Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues. Springer, Berlin (2000)

    Google Scholar 

  97. Sundblad, H.: Automatic acquisition of hyponyms and meronyms from question corpora. In: Proceedings of the Workshop on Natural Language Processing and Machine Learning for Ontology Engineering at ECAI’2002. Lyon, France (2003)

    Google Scholar 

  98. Torii, M., Liu, H., Hu, Z., Wu, C.: A comparison study of biomedical short form definition detection algorithms. In: TMBIO ’06: Proceedings of the 1st International Workshop on Text Mining in Bioinformatics, pp. 52–59. ACM, New York (2006)

    Chapter  Google Scholar 

  99. Uschold, M., Gruninger, M.: Ontologies and semantics for seamless connectivity. SIGMOD Record 33(4), 58–64 (2004)

    Article  Google Scholar 

  100. Velardi, P., Fabriani, P., Missikoff, M.: Using text processing techniques to automatically enrich a domain ontology. In: Proceedings of the International Conference on Formal Ontology in Information Systems (FOIS) (2001)

    Google Scholar 

  101. Vieira, R., Poesio, M.: An empirically based system for processing definite descriptions. Computational Linguistics 26(4), 539–593 (2000)

    Article  Google Scholar 

  102. Vossen, P.: Introduction to EuroWordNet. Computers and the Humanities 32(2–3), 73–89 (1998)

    Article  Google Scholar 

  103. Wermter, J., Hahn, U.: Paradigmatic modifiability statistics for the extraction of complex multi-word terms. In: HLT ’05: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 843–850, Association for Computational Linguistics, Morristown, NJ, USA (2005)

    Google Scholar 

  104. Wermter, J., Hahn, U.: You can’t beat frequency (unless you use linguistic knowledge): a qualitative evaluation of association measures for collocation and term extraction. In: ACL ’06: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL, pp. 785–792, Association for Computational Linguistics, Morristown, NJ, USA (2006)

    Google Scholar 

  105. Widdows, D.: Unsupervised method for developing taxonomies by combining syntactic and statistical information. In: Proceedings of HLT/NAACL (2003)

    Google Scholar 

  106. Witschel, H.F.: Using decision trees and text mining techniques for extending taxonomies. In: Proceedings of Learning and Extending Lexical Ontologies by using Machine Learning Methods, Workshop at ICML-05 (2005)

    Google Scholar 

  107. Xu, F., Kurz, D., Piskorski, J., Schmeier, S.: A domain adaptive approach to automatic acquisition of domain relevant terms and their relations with bootstrapping. In: Proceedings of the 3rd International Conference on Language Resources an Evaluation (LREC’02), Las Palmas, Canary Islands, Spain (2002)

    Google Scholar 

  108. Yamada, I., Baldwin, T.: Automatic discovery of telic and agentive roles from corpus data. In: Proceedings of the 18th Pacific Asia Conference on Language, Information and Computation (PACLIC 18) (2004)

    Google Scholar 

  109. Yarowsky, D.: Word-sense disambiguation using statistical models of Roget’s categories trained on large corpora. In: Proceedings of COLING-92, Nantes, France (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Poznan University of Economics, Al. Niepodleglosci 10, Poznan, Poland

    Marek Wisniewski

Authors
  1. Marek Wisniewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. INSA de Lyon, avenue Jean Capelle 7, Villeurbanne CX, 69621, France

    Youakim Badr

  2. Fac. Sciences Mirande, UMR CNRS 5158, Université de Bourgogne, Dijon CX, France

    Richard Chbeir

  3. Technology, Center for Quantifiable Quality of, Norwegian University of Science &, O.S. Bragstads plass 2E, Trondheim, 7491, Norway

    Ajith Abraham

  4. College of Business & Administration, Dept. Quantitative Methods &, Kuwait University, Safat, Kuwait

    Aboul-Ella Hassanien

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag London

About this chapter

Cite this chapter

Wisniewski, M. (2010). Metamodel of Ontology Learning from Text. In: Badr, Y., Chbeir, R., Abraham, A., Hassanien, AE. (eds) Emergent Web Intelligence: Advanced Semantic Technologies. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-077-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-84996-077-9_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-076-2

  • Online ISBN: 978-1-84996-077-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation