Abstract
Textbooks are educational documents created, structured and formatted by domain experts with the main 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 extendable approach towards automated extraction of this knowledge from textbooks taking into account their formatting rules and internal structure. We focus on PDF as the most common textbook representation format; however, the overall method is applicable to other formats as well. The evaluation experiments examine the accuracy of the approach, as well as the pragmatic quality of the obtained knowledge models using one of their possible applications - semantic linking of textbooks in the same domain. The results indicate high accuracy of model construction on symbolic, syntactic and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level.
| Original language | English |
|---|---|
| Pages | 1-10 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - 29 Sept 2020 |
| Event | 20th ACM Symposium on Document Engineering, DocEng 2020 - Virtual, Online, United States Duration: 29 Sept 2020 → 1 Oct 2020 |
Conference
| Conference | 20th ACM Symposium on Document Engineering, DocEng 2020 |
|---|---|
| Country/Territory | United States |
| City | Virtual, Online |
| Period | 29/09/20 → 1/10/20 |
Funding
This work was partially supported by the Ministry of Science, Technology and Telecommunication of Costa Rica (grant 2-1-4-17-1-021) and the INTERREG-IVA-GR program (grant 138 GR DeLux 32274).
Keywords
- knowledge modeling
- model extraction
- PDF processing
- textbook
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