Name
Abstract | ||
|---|---|---|
Question answering over knowledge bases (KBQA) is a challenging task in natural language processing. It requires machines to answer natural language questions based on large-scale knowledge bases. Recent years have witnessed remarkable success of neural network models on many natural language processing tasks, including KBQA. In this paper, we first review the recent advances of deep learning methods on solving simple questions in two streams, the information extraction style and semantic parsing style. We then introduce how to extend the neural architectures to answer more complex questions with iteration and decomposition techniques, and summarize current research challenges. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.aiopen.2021.12.001 | AI Open |
Keywords | DocType | Volume |
Deep learning,Question answering,Knowledge base | Journal | 2 |
ISSN | Citations | PageRank |
2666-6510 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Zhang | 1 | 0 | 1.01 |
| Yuxuan Lai | 2 | 0 | 1.69 |
| Yansong Feng | 3 | 735 | 64.17 |
| Dongyan Zhao | 4 | 998 | 96.35 |