# Data and domain knowledge dual‐driven artificial intelligence: Survey, applications, and challenges

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/data-and-domain-knowledge-dual-driven-artificial-intelligence-survey/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Jing Nie,Jiachen Jiang,Yang Li,Huting Wang,Sezai Erċışlı,Linze Lv |
| Citations | 52 |
| DOI | 10.1111/exsy.13425 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | bronze |
| OA URL | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/exsy.13425 |
| OpenAlex ID | https://openalex.org/W4385803650 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Jing Nie](https://scholariq.org/researchers/jing-nie/)

## Paper primary topic

- [Domain Adaptation and Few-Shot Learning](https://scholariq.org/topics/domain-adaptation-and-few-shot-learning/)

## Paper topics

- [Domain Adaptation and Few-Shot Learning](https://scholariq.org/topics/domain-adaptation-and-few-shot-learning/)
- [Machine Learning and Data Classification](https://scholariq.org/topics/machine-learning-and-data-classification/)
- [Explainable Artificial Intelligence (XAI)](https://scholariq.org/topics/explainable-artificial-intelligence-xai/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.