computer science

Machine Learning and Knowledge Extraction

Machine Learning and Knowledge Extraction is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Machine Learning and Knowledge Extraction rather than just read about it. In short: 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.

Key takeaways

Machine Learning and Knowledge Extraction belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Machine Learning and Knowledge Extraction to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Machine Learning and Knowledge Extraction from memory before moving on to harder problems.

Reference excerpt

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:

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

References

External links Official website

Worked examples

Example 1 — a first encounter with Machine Learning and Knowledge Extraction

Start with the simplest possible case. Write down what Machine Learning and Knowledge Extraction claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Machine Learning and Knowledge Extraction before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Machine Learning and Knowledge Extraction ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Machine Learning and Knowledge Extraction

In research: Machine Learning and Knowledge Extraction appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Machine Learning and Knowledge Extraction in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Machine Learning and Knowledge Extraction is common in secondary-school and first-year university syllabi. It links to neighbouring topics Academic journals established in 2019, Computer science journals, Creative Commons Attribution-licensed journals, so understanding it makes those chapters shorter.
In everyday life: Look for Machine Learning and Knowledge Extraction outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Machine Learning and Knowledge Extraction in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Machine Learning and Knowledge Extraction means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Machine Learning and Knowledge Extraction out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Machine Learning and Knowledge Extraction in simple terms?

Why does Machine Learning and Knowledge Extraction matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Machine Learning and Knowledge Extraction?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Machine Learning and Knowledge Extraction.