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Declarative Knowledge Extraction in the AC&NL Tutor

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Adaptive Instructional Systems (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12214))

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Abstract

Automatic knowledge acquisition is a rather complex and challenging task. This paper focuses on the description and evaluation of a semi-automatic authoring tool (SAAT) that has been developed as a part of the Adaptive Courseware based on Natural Language AC&NL Tutor project. The SAAT analyzes a natural language text and, as a result of the declarative knowledge extraction process, it generates domain knowledge that is presented in a form of natural language sentences, questions and domain knowledge graphs. Generated domain knowledge presents expert knowledge in the intelligent tutoring system Tutomat. The natural language processing techniques are applied and the tool’s functionalities are thoroughly explained. This tool is, to our knowledge, the only one that enables natural language question and sentence generation of different levels of complexity. Using an unstructured and unprocessed Wikipedia text in computer science, evaluation of domain knowledge extraction algorithm, i.e. the correctness of extraction outcomes and the effectiveness of extraction methods, was performed. The SAAT outputs were compared with the gold standard, manually developed by two experts. The results showed that 68.7% of detected errors referred to the performance of the integrated linguistic resources, such as CoreNLP, Senna, WordNet, whereas 31.3% of errors referred to the proposed extraction algorithms.

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Acknowledgments

The presented results are the outcome of the research projects “Adaptive Courseware based on Natural Language Processing (AC & NL Tutor)” and “Enhancing Adaptive Courseware based on Natural Language Processing” undertaken with the support of the United States Office of Naval Research Grants N00014-15-1-2789 and N00014-20-1-2066.

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Authors and Affiliations

  1. Faculty of Science, University of Split, Split, Croatia

    Ani Grubišić, Slavomir Stankov, Branko Žitko & Ines Šarić-Grgić

  2. Catholic Faculty of Theology, University of Split, Split, Croatia

    Angelina Gašpar

  3. Faculty of Humanities and Social Sciences, University of Split, Split, Croatia

    Suzana Tomaš

  4. Faculty of Science and Education, University of Mostar, Mostar, Bosnia and Herzegovina

    Emil Brajković & Daniel Vasić

Authors
  1. Ani Grubišić
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  2. Slavomir Stankov
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  3. Branko Žitko
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  4. Ines Šarić-Grgić
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  5. Angelina Gašpar
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  6. Suzana Tomaš
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  7. Emil Brajković
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  8. Daniel Vasić
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Correspondence to Ani Grubišić .

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Editors and Affiliations

  1. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  2. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Grubišić, A. et al. (2020). Declarative Knowledge Extraction in the AC&NL Tutor. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science(), vol 12214. Springer, Cham. https://doi.org/10.1007/978-3-030-50788-6_22

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  • DOI: https://doi.org/10.1007/978-3-030-50788-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50787-9

  • Online ISBN: 978-3-030-50788-6

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