Back to Search
Start Over
CogNLG: Cognitive graph for KG‐to‐text generation.
- Source :
-
Expert Systems . Jan2024, Vol. 41 Issue 1, p1-17. 17p. - Publication Year :
- 2024
-
Abstract
- Knowledge graph (KG) has been fully considered in natural language generation (NLG) tasks. A KG can help models generate controllable text and achieve better performance. However, most existing related approaches still lack explainability and scalability in large‐scale knowledge reasoning. In this work, we propose a novel CogNLG framework for KG‐to‐text generation tasks. Our CogNLG is implemented based on the dual‐process theory in cognitive science. It consists of two systems: one system acts as the analytic system for knowledge extraction, and another is the perceptual system for text generation by using existing knowledge. During text generation, CogNLG provides a visible and explainable reasoning path. Our framework shows excellent performance on all datasets and achieves a BLEU score of 36.7, which increases by 6.7 compared to the best competitor. [ABSTRACT FROM AUTHOR]
- Subjects :
- *KNOWLEDGE graphs
*COGNITIVE science
*NATURAL languages
*SCALABILITY
Subjects
Details
- Language :
- English
- ISSN :
- 02664720
- Volume :
- 41
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Expert Systems
- Publication Type :
- Academic Journal
- Accession number :
- 174037859
- Full Text :
- https://doi.org/10.1111/exsy.13461