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An empirically validated, onomasiologically structured, and linguistically motivated online terminology

Re-designing scientific resources on German grammar

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Abstract

Terminological resources play a central role in the organization and retrieval of scientific texts. Both simple keyword lists and advanced modelings of relationships between terminological concepts can make a most valuable contribution to the analysis, classification, and finding of appropriate digital documents, either on the web or within local repositories. This seems especially true for long-established scientific fields with elusive theoretical and historical branches, where the use of terminology within documents from different origins is often far from being consistent. In this paper, we report on the progress of a linguistically motivated project on the onomasiological re-modeling of the terminological resources for the grammatical information system grammis. We present the design principles and the results of their application. In particular, we focus on new features for the authoring backend and discuss how these innovations help to evaluate existing, loosely structured terminological content, as well as to efficiently deal with automatic term extraction. Furthermore, we introduce a transformation to a future SKOS representation. We conclude with a positioning of our resources with regard to the Knowledge Organization discourse and discuss how a highly complex information environment like grammis benefits from the re-designed terminological KOS.

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Notes

  1. See [36]; the information system can be found online at http://www.ids-mannheim.de/grammis/.

  2. http://www.ids-mannheim.de/progr@mm/.

  3. https://www.w3.org/TR/2009/REC-skos-reference-20090818/.

  4. https://www.w3.org/community/ontolex/wiki/Final_Model_Specification.

  5. Some of the results we already discussed in other publications in greater detail; we make reference to those in the corresponding sections.

  6. This distinction is inspired by lexicography where macrostructure is defined as the usually alphabetical order of entries and microstructure as the actual structure of one entry of a lexicon (cf. [18, p. 372]). Note that our macrostructure is ordered by concept relations instead of alphabetically.

  7. Note, however, that the application of visualization techniques is not limited to the traditional approach as is illustrated by EcoLexicon [28], a resource dealing with environmental terminology, based in the paradigm of frame-based terminology (cf. [14]).

  8. Note that both modes—hierarchical and non-hierarchical—are available to all types of relations, i.e., what is often known as hierarchical relations BT/NT can also be displayed as non-hierarchical network in our tool. The inclusion of non-hierarchical relations (RT) in a hierarchical tree, however, renders the resulting graph overly complex and hardly interpretable.

  9. This feature was recently added as a result of a usability study by Fu et al. [17]. They find that depending on the area of application it can be more beneficial to use indented trees or node-link diagrams (graphs) as visualization technique; hence, Fu et al. conclude that in designing a visualization tool, multiple visualization techniques should be combined. Furthermore, they indicate the importance of customization to enable the user to adapt the visualization to their needs.

  10. If the current concept is labeled Supplement (‘supplement’), this method will propose Adverbialsupplement (‘adverbial supplement’) as a candidate because the former is a substring of the latter.

  11. https://www.coreon.com/.

  12. http://www.dog-gmbh.de/de/produkte/lookup/.

  13. https://www.kaleidoscope.at/de/terminologie/quickterm.

  14. http://www.interverbumtech.de/.

  15. As reference corpus we used a sample from DeReKo corpus (cf. [26]) which covers various text types and genres.

  16. Ahmad et al.: [1, p. 720]:

    $$\begin{aligned} \frac{w_\mathrm{s}/t_\mathrm{s}}{w_\mathrm{g}/t_\mathrm{g}} \end{aligned}$$

    Note that Ahmad et al. use slightly different labels for the corpora: \(w_\mathrm{s}\) refers to the frequency of a word in the specialist language corpus (the domain-specific target corpus), \(w_\mathrm{g}\) to the frequency of a word in the general language corpus (the non-specialized language reference corpus), \(t_\mathrm{s}\) to the total count of words in the specialist language corpus, and \(t_\mathrm{g}\) refers to the total count of words in the general language corpus.

  17. For a detailed description of our approach to these theory tags, see [41, pp. 207–211].

  18. http://d2rq.org/.

  19. https://www.w3.org/TR/r2rml/.

  20. https://github.com/d2rq/d2rq/issues/45.

  21. For instance, clashes of associative and hierarchical links, see example 27 in the SKOS reference, https://www.w3.org/TR/2009/REC-skos-reference-20090818.

  22. Prospectively, the end-users of grammis might also benefit from visualization techniques, similar to those described in 3.2.1.

  23. https://grammis.ids-mannheim.de/fragen/3185.

  24. https://grammis.ids-mannheim.de/fragen/4550.

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Authors and Affiliations

  1. Technische Hochschule Köln, Cologne, Germany

    Karolina Suchowolec

  2. Institut für Deutsche Sprache (IDS), Mannheim, Germany

    Christian Lang & Roman Schneider

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  1. Karolina Suchowolec
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  2. Christian Lang
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Suchowolec, K., Lang, C. & Schneider, R. An empirically validated, onomasiologically structured, and linguistically motivated online terminology. Int J Digit Libr 20, 253–268 (2019). https://doi.org/10.1007/s00799-018-0254-x

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