TY - GEN
T1 - Towards the identification of concept prerequisites via knowledge graphs
AU - Manrique, Rubén
AU - Nunes, Bernardo Pereira
AU - Marino, Olga
AU - Cardozo, Nicolás
AU - Wolfgand, Sean
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Learning basic concepts before complex ones is a natural form of learning. This paper addresses the specific problem of identifying concept prerequisites to inform about the basic knowledge required to understand a particular concept. Briefly, given a target concept c, the goal is to (a) find candidate concepts in a Knowledge Graph (KG) that serve as possible prerequisite for c; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our approach explores the DBpedia Knowledge Graph and its semantic relations to find candidate concepts as well as a pruning step to reduce the candidate concept set. Finally, we employ supervised learning algorithms to evaluate and generate a list of prerequisites for the target concept. A ground truth created based on expert knowledge is used to validate our approach, exhibiting promising results with a precision varying between 83% and 92.9%.
AB - Learning basic concepts before complex ones is a natural form of learning. This paper addresses the specific problem of identifying concept prerequisites to inform about the basic knowledge required to understand a particular concept. Briefly, given a target concept c, the goal is to (a) find candidate concepts in a Knowledge Graph (KG) that serve as possible prerequisite for c; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our approach explores the DBpedia Knowledge Graph and its semantic relations to find candidate concepts as well as a pruning step to reduce the candidate concept set. Finally, we employ supervised learning algorithms to evaluate and generate a list of prerequisites for the target concept. A ground truth created based on expert knowledge is used to validate our approach, exhibiting promising results with a precision varying between 83% and 92.9%.
KW - Concept prerequisite identification
KW - Knowledge graphs
UR - http://www.scopus.com/inward/record.url?scp=85072935607&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2019.00101
DO - 10.1109/ICALT.2019.00101
M3 - Conference contribution
T3 - Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019
SP - 332
EP - 336
BT - Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019
A2 - Chang, Maiga
A2 - Sampson, Demetrios G
A2 - Huang, Ronghuai
A2 - Gomes, Alex Sandro
A2 - Chen, Nian-Shing
A2 - Bittencourt, Ig Ibert
A2 - Kinshuk, Kinshuk
A2 - Dermeval, Diego
A2 - Bittencourt, Ibsen Mateus
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Advanced Learning Technologies, ICALT 2019
Y2 - 15 July 2019 through 18 July 2019
ER -