• Bücheler, T., & Sieg, J. H. (2011). Understanding Science 2.0: Crowdsourcing and Open Innovation in the Scientific Method. Procedia Computer Science, 7, 327329. doi: 10.1016/j.procs.2011.09.014
  • Corby, O., & Faron-Zucker, C. (2002). Corese: A corporate semantic web engine. Paper presented at the Workshop on Real World RDF and Semantic Web Applications, WWW Conference, Hawaii.
  • Fikes, R., Hayes, P., & Horrocks, I. (2004). OWL-QL-a language for deductive query answering on the Semantic Web. Web Semant., 2(1), 1929. doi: 10.1016/j.websem.2004.07.002
  • Greenberg, J., Pattuelli, M. C., Parsia, B., & Robertson, W. D. (2001). Author-generated Dublin Core metadata for web resources: a baseline study in an organization. Journal of Digital Information, 2(2), 3846.
  • Hirohata, K., Okazaki, N., Ananiadou, S., & Ishizuka, M. (2008). Identifying Sections in Scientific Abstracts using Conditional Random Fields (pp. 381388).
  • Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20(4), 422446.
  • Liakata, M., Teufel, S., Siddharthan, A., & Batchelor, C. (2010). Corpora for the Conceptualisation and Zoning of Scientific Papers. Paper presented at the 7th International conference on Language Resources and Evaluation.
  • Lin, J., & Demner-Fushman, D. (2006). The role of knowledge in conceptual retrieval: a study in the domain of clinical medicine. Paper presented at the Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, Washington, USA.
  • Lindberg, D. A., Humphreys, B. L., & McCray, A. T. (1993). The Unified Medical Language System. Methods of information in Medicine, 32(4), 281.
  • Liu, X., Qin, J., & Chen, M. (2011). ScholarWiki system for knowledge indexing and retrieval. Proceedings of the American Society for Information Science and Technology, 48(1), 14. doi: 10.1002/meet.2011.14504801230
  • Lowe, H. J., & Barnett, G. O. (1994). Understanding and using the medical subject headings (MeSH) vocabulary to perform literature searches. JAMA: the journal of the American Medical Association, 271(14), 11031108.
  • Manning, C. D., Raghavan, P., & Schtze, H. (2008). Introduction to Information Retrieval: Cambridge University Press.
  • Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., & Stumme, G. (2009). Evaluating Similarity Measures for Emergent Semantics of Social Tagging. Paper presented at the 18th International World Wide Web Conference, Madrid, Spain.
  • Martin, P., & Eklund, P. W. (2000). Knowledge Retrieval and the World Wide Web. IEEE Intelligent Systems, 15(3), 1825. doi: 10.1109/5254.846281
  • Milstead, J., & Feldman, S. (1999). Metadata: Cataloging by Any Other Name. ONLINE-WESTON THEN WILTON-, 23, 2431.
  • Quinlan, J. R. (1993). C4.5: programs for machine learning. San Francisco, CA: Morgan Kaufmann Publishers.
  • Rodriguez, M. A., Bollen, J., & Sompel, H. V. D. (2009). Automatic metadata generation using associative networks. ACM Trans. Inf. Syst., 27(2), 120. doi: 10.1145/1462198.1462199
  • Sah, M., & Wade, V. (2011). Automatic mining of cognitive metadata using fuzzy inference. Paper presented at the Proceedings of the 22nd ACM conference on Hypertext and hypermedia, Eindhoven, The Netherlands.
  • Teufel, S., & Moens, M. (2002). Summarizing scientific articles: experiments with relevance and rhetorical status. Comput. Linguist., 28(4), 409445. doi: 10.1162/089120102762671936
  • Tonkin, E., & Muller, H. L. (2008). Semi automated metadata extraction for preprints archives. Paper presented at the Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, Pittsburgh PA, PA, USA