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Towards the Identification of Propaedeutic Relations in Textbooks

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Artificial Intelligence in Education (AIED 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11625))

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Abstract

As well-known, structuring knowledge and digital content has a tremendous potential to enhance meaningful learning. A straightforward approach is representing key concepts of the subject matter and organizing them in a knowledge structure by means of semantic relations. This results in hypergraphs with typed n-ary relationships, including the so-called prerequisite or propaedeutic relations among concepts. While extracting the whole concept graph from a textbook is our final goal, the focus of this paper is the identification of the propaedeutic relations among concepts. To this aim, we employ a method based on burst analysis and co-occurrence which recognizes, by means of temporal reasoning, prerequisite relations among concepts that share intense periods in the text. The experimental evaluation shows promising results for the extraction of propaedeutic relations without the support of external knowledge.

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Notes

  1. 1.

    Library pybursts, https://pypi.org/project/pybursts/0.1.1/.

  2. 2.

    Note that the current formula takes into account all the relations where an Allen’s pattern is recognized, while we are working on an improved version that limits them to relations where the subsidiary concept exhibits high burstiness.

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Author information

Authors and Affiliations

  1. Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy

    Giovanni Adorni, Chiara Alzetta, Frosina Koceva, Samuele Passalacqua & Ilaria Torre

Authors
  1. Giovanni Adorni
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  2. Chiara Alzetta
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  3. Frosina Koceva
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  4. Samuele Passalacqua
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  5. Ilaria Torre
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Corresponding author

Correspondence to Samuele Passalacqua .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, Sao Paulo, Brazil

    Seiji Isotani

  2. University of Malaga, Málaga, Spain

    Eva Millán

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Amy Ogan

  4. DePaul University, Chicago, IL, USA

    Peter Hastings

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  6. University College London, London, UK

    Rose Luckin

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Adorni, G., Alzetta, C., Koceva, F., Passalacqua, S., Torre, I. (2019). Towards the Identification of Propaedeutic Relations in Textbooks. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-23204-7_1

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