Challenges in generating bookmarks from TOC entries in e-books

Jayabal, Yogalakshmi; Ramanathan, Chandrashekar; Sheth, Mehul

doi:10.1145/2361354.2361363

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Third, we harvest title and authors from Google Books through its API using ISBN search 14 , which we believe is the best source of ISBN data. Querying using the 4905 ISBNs other well known sources, including Abebooks 15 , Amazon book 16 , ISBNSearch 17 , and BookFinder4U 18 , we retrieved 814, 1275, 1170, 1301 books respectively while Google Books returned 4329 books with valid title and authors and which covered all others.…”

Section: Metadata Extractionmentioning

confidence: 99%

“…In general, to effectively extract the ToC from a document, three sub-tasks need to be addressed: ToC detection, parsing and linking [14,23]. ToC detection attempts to locate the boundary of the ToC, usually based on explicit heuristics.…”

Section: Table Of Contents Extractionmentioning

confidence: 99%

“…However, those methods based on ad hoc rules only work for a small size and domainspecific book set. It is still a challenging problem to design a ToC recognition algorithm that can be effectively applied to large scale heterogeneous books [14]. Gao et al studied both ToC and metadata extraction from PDF book documents by modeling them as a matching problem on the bipartite graph [6].…”

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confidence: 99%

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Searching online book documents and analyzing book citations

Das

et al. 2013

Proceedings of the 2013 ACM Symposium on Document Engineering

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Academic search engines and digital libraries provide convenient online search and access facilities for scientific publications. However, most existing systems do not include books in their collections although several books are freely available online. Academic books are different from papers in terms of their length, contents and structure. We argue that accounting for academic books is important in understanding and assessing scientific impact. We introduce an open-book search engine that extracts and indexes metadata, contents, and bibliography from online PDF book documents. To the best of our knowledge, no previous work gives a systematical study on building a search engine for books.We propose a hybrid approach for extracting title and authors from a book that combines results from CiteSeer, a rule based extractor, and a SVM based extractor, leveraging web knowledge. For "table of contents" recognition, we propose rules based on multiple regularities based on numbering and ordering. In addition, we study bibliography extraction and citation parsing for a large dataset of books. Finally, we use the multiple fields available in books to rank books in response to search queries. Our system can effectively extract metadata and contents from large collections of online books and provides efficient book search and retrieval facilities.

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Section: Metadata Extractionmentioning

confidence: 99%

Section: Table Of Contents Extractionmentioning

confidence: 99%

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confidence: 99%

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Searching online book documents and analyzing book citations

Das

et al. 2013

Proceedings of the 2013 ACM Symposium on Document Engineering

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“…Organizational elements such as section headers and the ToC have been extracted using either a stand-alone rule-based method or in combination with machine learning. Ramanathan et al [226] presented an approach based only on rules and regular expressions to identify ToC entries. Wu et al [293] applied rules for entry detection according to three different ToC styles, as shown in Figure 2.7.…”

Section: Methodsmentioning

confidence: 99%

“…Fang et al [93] x x x Kruit et al [159] x x Gao et al [110] x x x Gao et al [108] x x x x x Ramanathan et al [226] x x x Wu et al [293] x x x Tkaczyk et al [265] x x x x x Wu et al [290] x x x x x Bast & Korzen [26] x x x x Tuarob et al [269,270] x x x x Larrañaga et al [165] x x Wali et al [280] x Lopes et al [171,172] x Dwarakanath et al [82] x Wang et al [284,285] x x Mihalcea & Csomai [195] x x Medelyan et al [189] x x Milne & Witten [197] x x Mendes et al [192] / Daiber et al [63] x x Moro et al [204] x x Zhu & Iglesias [302] x x Aghaebrahimian & Cielieback [5] x x…”

Section: Approachesmentioning

confidence: 99%

Extraction of Knowledge Models from Textbooks

Chacón¹

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Many adaptive educational systems and other artificial intelligence applications rely on high-quality knowledge representations. Still, knowledge acquisition remains the primary bottleneck hindering large-scale deployment and adoption of knowledge-based systems. One path to scalable knowledge extraction is using digital textbooks, given their domain-oriented content, structure, and availability. This dissertation presents a unified approach for automatically extracting high-quality and domain-specific knowledge models from digital textbooks. The proposed approach leverages the authors’ knowledge encoded in the textbooks’ elements that facilitate navigation and understanding of the material (table of contents, index, formatting styles) to create knowledge models. The proposed workflow first extracts initial information elements from the textbooks: the structure of chapters and subchapters using the Table of Content, the content of each section, and domain terminology from the back-of-the-book index. Then, new information is added: domain terms are linked to external entities in a knowledge graph (DBpedia) and are enriched with semantic content (e.g., abstracts and categories). Finally, the knowledge about the domain is refined by identifying the relevance of concepts to the target domain. The extracted knowledge is represented in a model using the Text Encoding Initiative. Multiple evaluations show that the extracted knowledge models have high levels of quality across several properties: accuracy, semantics, coverage, specificity, cognitive validity, and granularity. Additionally, the approach is effective in multiple domains—for example, statistics, ancient philosophy, and Python programming. Finally, there are many potential applications for the extracted knowledge models. This dissertation presents three different educational systems supported by the knowledge models.

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Knowledge models from PDF textbooks

Alpizar-Chacon

Sosnovsky

2021

New Review of Hypermedia and Multimedia

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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.

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Challenges in generating bookmarks from TOC entries in e-books

Cited by 5 publications

References 11 publications

Searching online book documents and analyzing book citations

Searching online book documents and analyzing book citations

Extraction of Knowledge Models from Textbooks

Knowledge models from PDF textbooks

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