Semandix: Constructing a Knowledge Base According to a Text Comprehension Model
The current chapter presents a computational semantic tool called Semandix, which is based on a cognitive text comprehension model. The basic aim of this tool is to construct a semantic knowledge base of concepts and relations among them, in order to analyze free text responses, assess concept maps and provide a semantic dictionary of concepts categorized according to the structures of that cognitive model. Thus, its basic modules are: the ‘Semantic Dictionary’, the ‘Text Analyzer’, the ‘Concept Map Assessor’, and the ‘Administrator’. The enrichment of Semandix knowledge base is being realized through XML format files, extracted from concept mapping tools, as CmapTools, and ‘machine-readable’ dictionaries, as WordNet through the Visdic Editor. So far, Semandix implements some of the basic modules of a proposed free-text response assessment system. Future plans are the Semandix extension, in order to implement the other modules of the proposed system, and the formalization of the semantic content constructed to enrich its knowledge base.
KeywordsConcept mapping Knowledge base Text comprehension model WordNets
- Baudet, S., & Denhière, G. (1992). Lecture, comprehension de texte et science cognitive. de France, Paris: Presses Universiteraires.Google Scholar
- Blitsas, P., & Grigoriadou, M. (2008). Towards a knowledge-based free-text response assessment system. Proceedings of the IADIS international conference in cognition and exploratory learning in digital age (CELDA 2008), pp. 37–44, Freiburg, Germany, October 13–15, 2008.Google Scholar
- Blitsas, P., Papadopoulos, G., & Grigoriadou, M. (2009). How concept mapping can support technical systems understanding based on Denhière-Baudet text comprehension model. Proceedings of the 9th IEEE international conference on advanced learning technologies (ICALT 2009), pp. 352–354, Riga, 14–18 July 2009.Google Scholar
- Brookshear, G. (2006). Computer science: An overview. Pearson Addison Wesley, 9th Edit, ISBN 0321387015, Harlow United Kingdom.Google Scholar
- Graesser, A., & Tipping, P. (1999). Chapter 24: Understanding texts. In: W. Bechtel & G. Graham (Eds.), A Companion to cognitive science. Malden, MA: Blackwell.Google Scholar
- Gruber, T. (1995). Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human Computer Studies, 43, pp. 907–928.Google Scholar
- Horák, A., & Smrž, P. (2004). VisDic–Wordnet browsing and editing tool. Proceedings of the 2nd international wordnet conference – GWC 2004, pp. 136–141, Brno, Czech Republic: Masaryk University, ISBN 80-210-3302-9.Google Scholar
- Hull, D. A. (1996). Stemming algorithms: A case study for detailed evaluation. Journal of the American Society for Information Science, pp. 70–84.Google Scholar
- Kanejiya, D., Kumar, A., & Prasad, S. (2003). Automatic evaluation of students’ answers using syntactically enhanced LSA. Proceedings of the HLT-NAACL workshop on building educational applications using natural language processing (pp. 53–60). Edmonton, Canada.Google Scholar
- Kremizis, A., Konstantinidi, I., Papadaki, M., Keramidas, G., & Grigoriadou, M. (2007). Greek WordNet extension in the domain of psychology and computer science. Proceedings of the 8th Hellenic European research computer mathematics and its applications conference (HERCMA 2007), Economical University, Athens. Retrieved February 2008, from http://www.aueb.gr/ pympe/hercma/proceedings2007/.
- Landauer, T. K. (2002). On the computational basis of learning and cognition: Arguments from LSA. In B. H. Ross (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 41, pp. 43–84). San Diego, CA: Academic.Google Scholar
- Lovins. J. B. (1968). Development of a stemming algorithm. Mechanical Translation and Computational Linguistics, 11, pp. 22–31.Google Scholar
- Namjoshi, K., & Kurshan, R. (1999). Efficient analysis of cyclic definitions. Proceedings of the 11th international conference on computer aided verification, pp. 394–405 ISBN 3-540-66202-2.Google Scholar
- Novak, J., & Gowin, B. (1984). Learning how to learn. New York: Cambridge University Press.Google Scholar
- Novak, J., & Musonda, D. (1991). A twelve-year longitudinal study of science concept learning. American Educational Research Journal, 28(1), pp. 117–153.Google Scholar
- Papakostas, E., & Stavropoulos, S. (2009). Ispell. Distribution under usage licences GPL/MPL/LGPL. Retrieved November 2009, from http://elspell.math.upatras.gr/ (in greek).
- Rinaldi, F., Dowdall, J., Hess M., Molla, D., Schwitter, R. (2002). Towards response extraction: An application to technical domains. In F. van Harmelen (Eds.), Proceeding of the 15th European conference on artificial intelligence, pp. 460–464, Amsterdam: IOS Press.Google Scholar
- Steinberger, J., & Ježek, K. (2004). Using latent semantic analysis in text summarization and summary evaluation. In Proceedings of ISIM 2004, Roznov pod Radhostem, Czech Republic, April 2004, pp. 93–100.Google Scholar