Knowledge models from PDF textbooks

Publication date

2021

Authors

Alpizar-Chacon, IsaacORCID 0000-0002-6931-9787ISNI 0000000506317436
Sosnovsky, S.A.ISNI 0000000352729779

Editors

Advisors

Supervisors

Document Type

Article
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License

cc_by_nc_nd

Abstract

Textbooks are educational documents created, structured and formatted by domain experts with the primary purpose to explain the knowledge in the domain to a novice. Authors use their understanding of the domain when structuring and formatting the content of a textbook to facilitate this explanation. As a result, the formatting and structural elements of textbooks carry the elements of domain knowledge implicitly encoded by their authors. Our paper presents an extensible approach towards automated extraction of knowledge models from textbooks and enrichment of their content with additional links (both internal and external). The textbooks themselves essentially become hypertext documents where individual pages are annotated with important concepts in the domain. The evaluation experiments examine several aspects and stages of the approach, including the accuracy of model extraction, the pragmatic quality of extracted models using one of their possible applications— semantic linking of textbooks in the same domain, the accuracy of linking models to external knowledge sources and the effect of integration of multiple textbooks from the same domain. The results indicate high accuracy of model extraction on symbolic, syntactic and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level.

Keywords

DBpedia, PDF processing, Textbook, knowledge modelling, model extraction, named entity disambiguation, semantic linking, Information Systems, Media Technology, Computer Science Applications

Citation

Alpizar-Chacon, I & Sosnovsky, S 2021, 'Knowledge models from PDF textbooks', New Review of Hypermedia and Multimedia, vol. 27, no. 1-2, pp. 128-176. https://doi.org/10.1080/13614568.2021.1889692