Name
Abstract | ||
|---|---|---|
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%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICALT.2019.00101 | 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) |
Keywords | Field | DocType |
Concept prerequisite identification, Knowledge graphs | Knowledge graph,Computer science,Supervised learning,Ground truth,Artificial intelligence,Supervised training,Multimedia,Machine learning | Conference |
Volume | ISSN | ISBN |
2161-377X | 2161-3761 | 978-1-7281-3486-4 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rubén Manrique | 1 | 4 | 2.19 |
| Bernardo Pereira Nunes | 2 | 185 | 30.96 |
| Olga Mariño | 3 | 0 | 0.34 |
| Nicolás Cardozo | 4 | 2 | 1.06 |
| Sean W. M. Siqueira | 5 | 0 | 0.34 |