Plagiarism Detection Using Semantic Knowledge Graphs

Khadilkar, Kunal; Kulkarni, Siddhivinayak; Bone, Poojarani

doi:10.1109/iccubea.2018.8697404

Cited by 5 publications

(2 citation statements)

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“…In order to evaluate the relation extraction, the algorithm XGBoost was used for binary classification. In several other investigations, entity extraction was cast as a classification problem which was addressed using algorithms like Support Vector Machine (SVM) [31] and Conditional Random Field classifier (CRFClassifier) [32]. Other methodologies focused on entity extraction using semantic similarity [33] or Stack Long Short Term Memory (Stack-LSTM) with Spacy 1 [34].…”

Section: A Knowledge Acquisitionmentioning

confidence: 99%

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Knowledge Graphs in Education and Employability: A Survey on Applications and Techniques

2022

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Studies on the relationship between education and employability are of paramount importance for policy makers, training institutions, companies and students. The availability of large scale data on the internet, such as online job ads, has been leveraged to better investigate this relationship. Recent studies have shown that representing this data using knowledge graphs can be useful in identifying the job market needs and establishing better assessment methods. Such studies also highlight the skill mismatch between education and the job market and help mitigate it. In this survey, we provide a comprehensive review of the applications of knowledge graphs in education and employability. We go through the knowledge graph based frameworks that have been proposed in previous work on different aspects of education and employability. The survey introduces new taxonomies for these applications and their methodologies, and provides a thorough outlook on promising research directions. We also present a use case to illustrate how knowledge graphs can be used to investigate the relationship between education and the job market.

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Section: A Knowledge Acquisitionmentioning

confidence: 99%

“…Multiple studies tackle this aspect using KGs. [32] explored establishing teacher support methodologies where the authors adopted a KG-based approach to detect plagiarism. The methodology constructs a semantic KG from unstructured data.…”

Section: Educational Assessmentmentioning

confidence: 99%