Mapping a Lexical Semantic Resource to a Common Framework of Computational Lexicons

  • Milena Slavcheva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7499)


In recent years the proliferation of language resources has brought up the question of their interoperability, reuse and integration. Currently, it is appropriate not only to produce a language resource, but to connect it to prominent frameworks and global infrastructures. This paper presents the mapping of SemInVeSt – a knowledge base of the semantics of verb-centred structures in Bulgarian, French and Hungarian, to the Lexical Markup Framework (LMF) – an abstract metamodel, providing a common, standardized framework for the representation of computational lexicons. SemInVeSt and LMF share their underlying models, that is, both are based on the four-layer metamodel architecture of the Unified Modeling Language (UML). A two-step mapping of the SemInVeSt and LMF models is considered: the first step provides an LMF conformant schema of SemInVeSt as a multilingual lexical resource with a reference to an external system containing the semantic descriptors of the lexical units; the second step implies an LMF conformant representation of the semantic descriptors themselves, which are a product of the application of the Unified Eventity Representation (UER) – a cognitive theoretical approach to verb semantics and a graphical formalism, based on UML.


lexical semantics multilingual resources verb-centred structures eventity frames 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zipser, F.S., Romary, L.: A model oriented approach to the mapping of annotation formats using standards. In: Proceedings of a Workshop on Language Resources and Language Technology Standards – State of the Art, Emerging Needs, and Future Developments, LREC 2010, Malta, pp. 7–18 (2010)Google Scholar
  2. 2.
    Cimiano, P., Buitelaar, P., McCrae, J., Sintek, M.: LexInfo: A declarative model for the lexicon-ontology interface. Journal of Web Semantics: Science, Services and Agents on the World Wide Web, 29–51 (2011)Google Scholar
  3. 3.
    LMF: ISO 24613: Language resource management – Lexical markup framework (LMF) (2008)Google Scholar
  4. 4.
    Francopoulo, G., Bel, N., George, M., Calzolari, N., Monachini, M., Pet, M., Soria, C.: Lexical markup framework: ISO standard for semantic information in NLP lexicons. In: (Gesellschaft fuer linguistische Datenverarbeitung) (GLDV), Tuebingen, Germany (2007)Google Scholar
  5. 5.
    Schalley, A.C.: Cognitive Modeling and Verbal Semantics. A Representational Framework Based on UML. In: Trends in Linguistics. Studies and Monographs, vol. 154, Mouton de Gruyter, Berlin (2004)Google Scholar
  6. 6.
    OMG: ISO/IEC 19501 Unified Modeling Language Specification, Version 1.4.2. Object Management Group (OMG) (2005),
  7. 7.
    Bel, N., Espeja, S., Marimon, M., Villegas, M.: COLDIC, a lexicographic platform for LMF compliant lexica. In: Proceedings of LREC 2008, Marrakech, Morocco (2008)Google Scholar
  8. 8.
    Maks, I., Tiberius, C., van Veenendaal, R.: Standardising bilingual lexical resources according to the Lexicon Markup Framework. In: Proceedings of LREC 2008, Marrakech, Morocco (2008)Google Scholar
  9. 9.
    Soria, C., Monachini, M.: Kyoto-LMF: WordNet Representation Format. KYOTO Working Paper: WP02 TR002 V4 Kyoto LMF (2008)Google Scholar
  10. 10.
    Soria, C., Monachini, M., Vossen, P.: Wordnet-LMF: Fleshing out a standardized format for wordnet interoperability. In: IWIC 2009: Proceedings of the 2009 International Workshop on Intercultural Collaboration, pp. 139–146. ACM, New York (2009)CrossRefGoogle Scholar
  11. 11.
    Henrich, V., Hinrichs, E.: Standardizing wordnets in the ISO standard LMF: Wordnet-LMF for GermaNet. In: Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), Beijing, China, pp. 456–464 (2010)Google Scholar
  12. 12.
    Goddard, C., Schalley, A.C.: Semantic analysis. In: Indurkhya, N., Damerau, F.J. (eds.) Handbook of Natural Language Processing. Chapman and Hall/CRC Machine Learning and Pattern Recognition, pp. 93–120. CRC Press / Taylor and Francis Group, Goshen, Connecticut, USA (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Milena Slavcheva
    • 1
  1. 1.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations