science

Knowledge acquisition

Knowledge acquisition is a 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 Knowledge acquisition rather than just read about it. In short: Knowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules, objects, and frame-based ontologies.

Key takeaways

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

Reference excerpt

Knowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules, objects, and frame-based ontologies. Expert systems were one of the first successful applications of artificial intelligence technology to real world business problems. Researchers at Stanford and other AI laboratories worked with doctors and other highly skilled experts to develop systems that could automate complex tasks such as medical diagnosis. Until this point computers had mostly been used to automate highly data intensive tasks but not for complex reasoning. Technologies such as inference engines allowed developers for the first time to tackle more complex problems. As expert systems scaled up from demonstration prototypes to industrial strength applications it was soon realized that the acquisition of domain expert knowledge was one of if not the most critical task in the knowledge engineering process. This knowledge acquisition process became an intense area of research on its own. One of the earlier works on the topic used Batesonian theories of learning to guide the process. One approach to knowledge acquisition investigated was to use natural language parsing and generation to facilitate knowledge acquisition. Natural language parsing could be performed on manuals and other expert documents and an initial first pass at the rules and objects could be developed automatically. Text generation was also extremely useful in generating explanations for system behavior. This greatly facilitated the development and maintenance of expert systems. A more recent approach to knowledge acquisition is a re-use based approach. Knowledge can be developed in ontologies that conform to standards such as the Web Ontology Language (OWL). In this way knowledge can be standardized and shared across a broad community of knowledge workers. One example domain where this approach has been successful is bioinformatics.

Applications Knowledge acquisition has been applied to many fields beyond expert systems, including natural language processing, decision support systems, training simulations, and intelligent tutoring systems. It also plays an essential role in building knowledge-based agents and cognitive architectures.

Techniques A variety of techniques exist for knowledge acquisition, ranging from manual to automatic methods. Manual approaches include structured interviews, protocol analysis, card sorting, and repertory grid analysis. Automated approaches include machine learning, data mining, and natural language processing.

See also Knowledge collection from volunteer contributors – Subfield of AI Knowledge extraction – Creation of knowledge from structured and unstructured sources Information processing (psychology) – Approach to understanding human thinking Information search (disambiguation)

References 8. Informatika.web.id "Akuisisi Pengetahuan (Knowledge Acquisition) Sistem Pakar"

Illustrations

Worked examples

Example 1 — a first encounter with Knowledge acquisition

Start with the simplest possible case. Write down what Knowledge acquisition claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In 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 Knowledge acquisition 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 Knowledge acquisition 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 Knowledge acquisition

In research: Knowledge acquisition appears in 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 Knowledge acquisition 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: Knowledge acquisition is common in secondary-school and first-year university syllabi. It links to neighbouring topics Expert systems, Knowledge economy, Knowledge sharing, so understanding it makes those chapters shorter.
In everyday life: Look for Knowledge acquisition 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.

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How to study Knowledge acquisition in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Knowledge acquisition 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 Knowledge acquisition out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Knowledge acquisition in simple terms?

Why does Knowledge acquisition matter?

Because it connects several 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 Knowledge acquisition?

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 Knowledge acquisition.