Correlating educational documents from different sources through graphs and taxonomies

Resumo


Digital educational documents are growing in size and variety, and scientists are facing difficulties to find their way through them. One of the initiatives that have emerged to solve this problem involves the use of automatic classification algorithms. However, it is difficult to analyze implicit relationships among topics of materials. This paper presents CIMAL, a framework for enabling flexible access to material stored in arbitrary repositories. CIMAL combines semantic classification, taxonomies and graphs to elicit relationships among topics of educational documents. We validated our work using materials from Coursera (courses offered by Johns Hopkins University and University of Michigan) and a Higher Education Institute, from Brazil.

Palavras-chave: Educational Documents, graphs, taxonomies, semantic classification

Referências

Angles, R. and Gutierrez, C. (2008). Survey of graph database models. ACM Comput. Surv., 40(1):1:1–1:39.

Beneventano, D., Gennaro, C., Bergamaschi, S., and Rabitti, F. (2011). A mediator-based approach for integrating heterogeneous multimedia sources. Multimedia Tools and Applications, 62(2):427–450.

Blei, D. M. (2012). Probabilistic topic models. Commun. ACM, 55(4):77–84.

Cavoto, P., Cardoso, V., Vignes Lebbe, R., and Santanche, A. (2015). FishGraph: A Network-Driven Data Analysis. In 11th IEEE Int. Conf. on eScience, Germany.

Changuel, S., Labroche, N., and Bouchon-Meunier, B. (2015). Resources sequencing using automatic prerequisite–outcome annotation. ACM Trans. Intell. Syst. Technol., 6(1):pages 6:1–6:30.

Codd, E. F. (1980). Data models in database management. SIGPLAN Not., 16(1):112–114.

Egozi, O., Markovitch, S., and Gabrilovich, E. (2011). Concept-based information retrieval using explicit semantic analysis. ACM Trans. Inf. Syst., 29(2):8:1–8:34.

Gabrilovich, E. and Markovitch, S. (2007). Computing semantic relatedness using wikipedia-based explicit semantic analysis. In IJCAI, pages 1606–1611, CA, USA. Morgan Kaufmann Publishers Inc.

Gabrilovich, E. and Markovitch, S. (2009). Wikipedia-based semantic interpretation for natural language processing. J. Artif. Int. Res., 34(1):443–498.

Gater, A., Grigori, D., and Bouzeghoub, M. (2011). A graph-based approach for semantic process model discovery. Graph Data Management, pages 438–462.

Jiang, J. (2012). Information extraction from text. In Aggarwal, C. C. and Zhai, C., editors, Mining Text Data, pages 11–41. Springer US.

Khan, A., Wu, Y., and Yan, X. (2012). Emerging graph queries in linked data. In ICDE, pages 1218–1221. IEEE.

Learning Technology Standards Committee of the IEEE (2002). Draft standard for learning technology - learning object metadata. Technical report, IEEE Standards Department, New York.

Little, S., Ferguson, R., and Ruger, S. (2012). Finding and reusing learning materials with multimedia similarity search and social networks. Technology, Pedagogy and Education, 21(2):pages 255–271.

Matos-Junior, O., Ziviani, N., Botelho, F. C., Cristo, M., Lacerda, A., and da Silva, A. S. (2012). Using taxonomies for product recommendation. JIDM, 3(2):pages 85–100.

Mishra, S., Gorai, A., Oberoi, T., and Ghosh, H. (2010). Efficient Visualization of Content and Contextual Information of an Online Multimedia Digital Library for Effective Browsing. WI-IAT2010, pages 257–260.

Mota, M. S. and Medeiros, C. B. (2013). Introducing shadows: Flexible document representation and annotation on the web. ICDE Workshops, pages 13–18.

Ouyang, Y. and Zhu, M. (2007). eLORM: Learning object relationship mining based repository. Proceedings - IEEE Int. Conf. on E-Commerce Technology and CEC/EEE, pages 691–698.

Pereira, B. (2014). Entity Linking with Multiple Knowledge Bases: An Ontology Modularization Approach. In ISWC, pages 513–520. Springer.

Ricarte, I. L. M. and Junior, G. R. F. (2011). A methodology for mining data from computer-supported learning environments. Informatica na educação: teoria & prática, 14(2).

Romero, C. and Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1):12–27.

Rossi, R. G., Rezende, S. O., and Lopes, A. A. (2015). Term network approach for transductive classification. volume 9042, pages 497–515. Springer International Publishing.

Santanche, A., Longo, J. S. C., Jomier, G., Zam, M., and Medeiros, C. B. (2014). Multifocus research and geospatial data anthropocentric concerns. JIDM, 5(2):pages 146–160.

Saraiva, M. C. and Medeiros, C. B. (2016). Use of graphs and taxonomic classifications to analyze content relationships among courseware. In Brazilian Symposium on Databases, SBBD 2016, Salvador, Bahia, Brazil, pages 265–270.

Saraiva, M. C. and Medeiros, C. B. (2017). Finding out topics in educational materials using their components. In 47th Annual IEEE Frontiers in Education Conference (FIE), Indianapolis, IN, USA, pp. 1-7.

Sathiyamurthy, K., Geetha, T. V., and Senthilvelan, M. (2012). An approach towards dynamic assembling of learning objects. In ICACCI, pages 1193–1198. ACM.

Silva, L. M. D. and Santanche, A. (2009). ARARA: Autoria de Objetos Digitais Complexos Baseada em Documentos. Simpósio Brasileiro de Informática na Educação, (2009):10.

Zhuang, Y. (2017). Bag-of-discriminative-words (bodw) representation via topic modeling. IEEE Transactions on Knowledge and Data Engineering, 29(5):977–990.
Publicado
25/08/2018
SARAIVA, Márcio de Carvalho; MEDEIROS, Claudia Bauzer. Correlating educational documents from different sources through graphs and taxonomies. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 33. , 2018, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 121-132. ISSN 2763-8979. DOI: https://doi.org/10.5753/sbbd.2018.22224.