Advertisement

Creating Linked Data for the Interdisciplinary International Collaborative Study of Language Acquisition and Use: Achievements and Challenges of a New Virtual Linguistics Lab

  • María Blume
  • Suzanne Flynn
  • Barbara Lust

Abstract

In this paper, we describe and exemplify our development of a cyber-tool, the Data Transcription and Analysis tool (DTA tool) that is currently being implemented in the Virtual Center for Language Acquisition through a Virtual Linguistic Lab (VLL). We review this cyber-tool’s design and accomplishments to date, assessing its ability to address. We explicate the architecture and usability of the DTA tool, we summarize its current status, possibilities for expansion, and related challenges we currently confront. We focus on the conceptual and functional structure of this tool here, and not on technical aspects of its programming.

Keywords

Resource Description Framework Link Data Language Acquisition Language Data Speech Mode 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Berners-Lee T (2009). URL http://en.wikipedia.org/wiki/Linked_data, Ted Lecture. Tim Berners-Lee on the next Web
  2. Bickel B, Comrie B, Haspelmath M (2008) Glossing rules: Conventions for interlinear morpheme-by-morpheme glosses. URL http://www.eva.mpg.de/lingua/resources/glossing-rules.php
  3. Blume M, Lust B (2011) Data Transcription and Analysis Tool User’s Manual. With the collaboration of Shamitha Somashekar, and Tina Ogden Google Scholar
  4. Blume M, Yang S, Lust B (in prep) Virtual Linguistics Lab (VLL) Research Methods Manual: Scientific Methods for Study of Language Acquisition. With the collaboration of Tina Ogden, Shamitha Somashekar, Yuchin Chien, Liliana Sánchez, Claire Foley, Marina Kalashnikova, Martha Rayas, Yarden Kedar and Natalia Buitrago Google Scholar
  5. Chiarcos C, Hellmann S, Nordhoff S (this vol.) Introduction and overview. pp 1–12 Google Scholar
  6. Farrar S, Langendoen D (2003) A linguistic ontology for the semantic web. GLOT International 7:97–100 Google Scholar
  7. Khan H, Caruso B, Lowe B, Corson-Rikert J, Dietrich D, Steinhart G (2011) Datastar: Using the semantic web approach for data curation. International Journal of Digital Curation 2:209–221 Google Scholar
  8. Lowe B (2009) Datastar: Bridging XML and OWL in science metadata management. Metadata and Semantics Research 46:141–150, URL http://www.springerlink.com/content/q0825vj78ul38712/ MathSciNetCrossRefGoogle Scholar
  9. Lust B, Flynn S, Blume M, Westbrooks E, Tobin T (2010) Constructing adequate documentation for multi-faceted cross linguistic language data: A case study from a virtual center for study of language acquisition. In: Language Documentation: Theory, Practice and Values, John Benjamins, Amsterdam/Philadelphia, pp 127–152 Google Scholar
  10. Simons G, Lewis W, Farrar S, Langendoen D, Fitzsimons B, Gonzalez H (2004) The semantics of markup. In: Proc. 4th Workshop on NLP and XML (NLPXML-2004), Barcelona, Spain, pp 25–32 Google Scholar
  11. Steinhart G (2010) Datastar: A data staging repository to support the sharing and publication of research data. In: 31st Annual IATUL Conference - The Evolving World of e-Science: Impact and Implicationsfor Science and Technology Libraries, West Lafayette, URL http://docs.lib.purdue.edu/iatul2010/conf/day2/8/

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Languages and LinguisticsUniversity of Texas at El PasoEl PasoUSA

Personalised recommendations