Machine Learning and Knowledge Extraction

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
Open access	Yes
License	Creative Commons Attribution License
Impact factor	6.0 (2023)
Standard abbreviations ISO 4 (alt) · Bluebook (alt) NLM (alt) · MathSciNet (alt )
ISO 4	Mach. Learn. Knowl. Extr.
Indexing CODEN (alt) · JSTOR (alt) · LCCN (alt) MIAR  · NLM (alt) · Scopus  · W&L
ISSN	2504-4990
Links
Journal homepage

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]

• DOAJ ^[2]
• Emerging Sources Citation Index ^[3]
• Scopus ^[4]

According to the Journal Citation Reports , the journal has a 2024 impact factor of 6.0. ^[5]

References

1. "Machine Learning and Knowledge Extraction". MIAR: Information Matrix for the Analysis of Journals . University of Barcelona . Retrieved 2025-06-18.
2. "Machine Learning and Knowledge Extraction". Directory of Open Access Journals. Retrieved 2025-06-18.
3. "Machine Learning and Knowledge Extraction included in ESCI". Clarivate . Retrieved 2025-06-18.
4. "Scopus preview – Machine Learning and Knowledge Extraction". Scopus. Retrieved 2025-06-18.
5. "Pharmaceuticals". 2024 Journal Citation Reports. Web of Science (Science ed.). Clarivate. 2025.

External links

• Official website