Machine Learning and Knowledge Extraction
| Discipline | Machine learning, artificial intelligence, data science |
|---|---|
| Language | English |
| Edited by | Andreas Holzinger |
| Publication details | |
| History | 2019–present |
| Publisher | MDPI (Switzerland) |
| Frequency | Quarterly |
| Yes | |
| License | Creative Commons Attribution License |
| 6.0 (2023) | |
| Standard abbreviations | |
| ISO 4 | Mach. Learn. Knowl. Extr. |
| Indexing | |
| ISSN | 2504-4990 |
| Links | |
Machine Learning and Knowledge Extraction (MAKE) is a peer-reviewed open-access scientific journal covering research on machine learning, knowledge extraction and related areas of data-driven artificial intelligence. It is published by MDPI and was launched in 2019 with Andreas Holzinger as founding Editor-in-Chief.
The journal publishes research articles, reviews, tutorials and short notes spanning topics such as data ecosystems, interactive and automated machine learning, explainable AI, privacy, graph learning and topological data analysis
Abstracting and indexing
The journal is abstracted and indexed in several databases, for example in:[1]
According to the Journal Citation Reports, the journal has a 2024 impact factor of 6.0.[5]
References
- ^ "Machine Learning and Knowledge Extraction". MIAR: Information Matrix for the Analysis of Journals. University of Barcelona. Retrieved 2025-06-18.
- ^ "Machine Learning and Knowledge Extraction". Directory of Open Access Journals. Retrieved 2025-06-18.
- ^ "Machine Learning and Knowledge Extraction included in ESCI". Clarivate. Retrieved 2025-06-18.
- ^ "Scopus preview – Machine Learning and Knowledge Extraction". Scopus. Retrieved 2025-06-18.
- ^ "Pharmaceuticals". 2024 Journal Citation Reports. Web of Science (Science ed.). Clarivate. 2025.
External links
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