Expanding the Web of Knowledge

Alpizar-Chacon, Isaac; Sosnovsky, Sergey

doi:10.1145/3342220.3343671

Cited by 8 publications

(12 citation statements)

References 33 publications

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“…A term-based knowledge model of each textbook has been extracted by the presented approach. Additionally, they have been automatically mapped to the ISI Multilingual Glossary of Statistical Terms (ISI Glossary) 11 . The ISI Glossary translates more than 3500 statistical terms (and synonyms) into 31 languages 12 .…”

Section: Methodsmentioning

confidence: 99%

“…In the next phase ( Chapter 4), the domain terms are used as a bridge to link the textbooks to an external knowledge graph. Specifically, domain terms are matched to corresponding entities in DBpedia 11 -a publicly available knowledge graph based on Wikipedia. The linking with DBpedia entities allows for the enrichment of the domain terms with semantic information (abstracts, Wikipedia links, categories, synonyms, and relations to other terms).…”

Section: Approachmentioning

confidence: 99%

“…Using the information from E, it is possible to enrich the original textbook model to incorporate additional semantic information and create connections to the LOD using RDF relationships. Since the TEI Guidelines do not provide a mechanism to integrate RDF with TEI 11 , RDFa 12 attributes are added to the TEI attribute class att.global.linking: @about, @property and @resource [235,264]. Those attributes allow for representing RDF tuples: @about for the subject, @property predicate and @resource object.…”

Section: Enriched Tei Modelmentioning

confidence: 99%

“…The resulting kappa of 0.36 is considered a fair agreement. Given that all raters were experts and made their relevance assessments fully independently from each other, a smoothed factor 11 was used to compute the final relevance for each query-document pair. The average relevance was multiplied by 0.76, 0.91, and 1 for a document identified by one, two, or three raters, respectively.…”

Section: Datasets and Proceduresmentioning

confidence: 99%

“…https://lucene.apache.org/ 10 E.g., https://www.stat.berkeley.edu/~mgoldman/sylsm09.pdf11 Calculated using 1 + ((1 − support) ⋅ (log(support) ⋅ support)), which was inspired by the expected information gain formula used in decision trees.…”

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confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Enriched Tei Modelmentioning

confidence: 99%

Section: Datasets and Proceduresmentioning

confidence: 99%

mentioning

confidence: 99%

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Extraction of Knowledge Models from Textbooks

Chacón¹

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Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis

Alpizar-Chacon

Sosnovsky

Brusilovsky

2023

Lecture Notes in Computer Science

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This paper evaluates an automatically extracted domain model from textbooks and applies learning curve analysis to assess its ability to represent students' knowledge and learning. Results show that extracted concepts are meaningful knowledge components with varying granularity, depending on textbook authors' perspectives. The evaluation demonstrates the acceptable quality of the extracted domain model in knowledge modeling.

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Building datasets to support information extraction and structure parsing from electronic theses and dissertations

Ingram,

Wu,

Kahu

et al. 2024

Int J Digit Libr

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Despite the millions of electronic theses and dissertations (ETDs) publicly available online, digital library services for ETDs have not evolved past simple search and browse at the metadata level. We need better digital library services that allow users to discover and explore the content buried in these long documents. Recent advances in machine learning have shown promising results for decomposing documents into their constituent parts, but these models and techniques require data for training and evaluation. In this article, we present high-quality datasets to train, evaluate, and compare machine learning methods in tasks that are specifically suited to identify and extract key elements of ETD documents. We explain how we construct the datasets by manual labeling the data or by deriving labeled data through synthetic processes. We demonstrate how our datasets can be used to develop downstream applications and to evaluate, retrain, or fine-tune pre-trained machine learning models. We describe our ongoing work to compile benchmark datasets and exploit machine learning techniques to build intelligent digital libraries for ETDs.

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Expanding the Web of Knowledge

Cited by 8 publications

References 33 publications

Extraction of Knowledge Models from Textbooks

Extraction of Knowledge Models from Textbooks

Measuring the Quality of Domain Models Extracted from Textbooks with Learning Curves Analysis

Building datasets to support information extraction and structure parsing from electronic theses and dissertations

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