Distracting users as per their knowledge: Combining linked open data and word embeddings to enhance history learning

Blanco‐Fernández, Yolanda; Gil‐Solla, Alberto; Pazos‐Arias, José J.; Ramos‐Cabrer, Manuel; Daif, Abdullah; López‐Nores, Martín

doi:10.1016/j.eswa.2019.113051

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…This 1926 character-long text (hereafter denoted as T 0 ) is related to the Battle of Thermopylae among Greeks and Persians in 480 BC (Blanco et al, 2020). That is, the aim is to compose a corpus related to the Greco-Persian wars, starting from a brief text related to the second Persian invasion of Greece and specifically to the famous and inspiring Battle of Thermopylae.…”

Section: Strategy and Sources Of Informationmentioning

confidence: 99%

“…The learning of word embeddings has gained momentum in many Natural Language Processing (NLP) applications, ranging from text document summarisation (Mohd et al, 2020), fake news detection (Faustini and Covões, 2017;Silva et al, 2020), and term similarity measure (Lastra et al, 2019;Gali et al, 2019) to sentiment classification (Rezaeinia et al, 2019;Giatsoglou et al, 2017;Park et al, 2021), edutainment (Blanco et al, 2020), Named Entity Recognition (Turian et al, 2010;Gutiérrez-Batista et al, 2018), classification tasks (Jung et al, 2022) and personalization systems (Valcarce et al, 2019), just to name a few. Most popular methods consider a large corpus of texts and represent each word with a real-valued dense vector, which captures its meaning assuming that words sharing common contexts in the input corpus are semantically related to each other (and consequently their respective word vectors are close in the vector space) (Mikolov et al, 2013b).…”

Section: Introductionmentioning

confidence: 99%

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Automatically Assembling a Custom-Built Training Corpus for Improving the Learning of In-Domain Word/Document Embeddings

Blanco-Fernández,

Gil-Solla,

Pazos-Arias

et al. 2023

Informatica

Self Cite

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Embedding models turn words/documents into real-number vectors via co-occurrence data from unrelated texts. Crafting domain-specific embeddings from general corpora with limited domain vocabulary is challenging. Existing solutions retrain models on small domain datasets, overlooking potential of gathering rich in-domain texts. We exploit Named Entity Recognition and Doc2Vec for autonomous in-domain corpus creation. Our experiments compare models from general and in-domain corpora, highlighting that domain-specific training attains the best outcome.

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Section: Strategy and Sources Of Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatically Assembling a Custom-Built Training Corpus for Improving the Learning of In-Domain Word/Document Embeddings

Blanco-Fernández,

Gil-Solla,

Pazos-Arias

et al. 2023

Informatica

Self Cite

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show abstract

“…To make correspondence between two or more ontology associated with our knowledge representation system. In fact, some researchers use linked or open data for the representation of know-ledge based on the SPARQL language [14], whereas others use the ontological representation [15] as a solution to problems relating to knowledge mapping. Knowledge identification is achieved via the use of two types of methods, namely: non-supervised [14] and supervised Web methods processed by experts in the field [16].…”

Section: Ontological Engineeringmentioning

confidence: 99%

Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Hadioui

Touimi

Faddouli

et al. 2021

IJECE

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Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.

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Creating Automatic Connections for Personal Knowledge Management

Fraga,

Poggi,

Casanova

et al. 2024

SN COMPUT. SCI.

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Distracting users as per their knowledge: Combining linked open data and word embeddings to enhance history learning

Cited by 6 publications

References 14 publications

Automatically Assembling a Custom-Built Training Corpus for Improving the Learning of In-Domain Word/Document Embeddings

Automatically Assembling a Custom-Built Training Corpus for Improving the Learning of In-Domain Word/Document Embeddings

Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Creating Automatic Connections for Personal Knowledge Management

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