Knowledge Information Flashcards
7 cards from real AI practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Knowledge Information flashcards as text
Which knowledge representation technique uses probabilistic relationships between variables to model uncertainty?
Answer: Bayesian network
Bayesian networks use directed acyclic graphs to represent probabilistic dependencies between variables, enabling inference under uncertainty.
In a knowledge graph, what is the primary purpose of an 'ontology'?
Answer: Define concepts, relationships, and rules within a domain
An ontology formally defines the concepts, their properties, and relationships within a domain, providing a shared vocabulary and structure for the knowledge graph.
What is 'knowledge distillation' in the context of AI model compression?
Answer: Training a smaller student model to mimic a larger teacher model
Knowledge distillation transfers knowledge from a large, complex teacher model to a smaller student model by training the student on soft probability outputs from the teacher.
Which information retrieval metric measures the fraction of relevant documents returned out of all documents returned?
Answer: Precision
Precision measures how many of the retrieved documents are actually relevant, calculated as true positives divided by all retrieved documents.
In retrieval-augmented generation (RAG), what role does the 'retriever' component play?
Answer: Fetches relevant context documents to ground the generator's response
The retriever searches an external knowledge store for documents relevant to the query and passes them as context to the generative model.
What does 'knowledge grounding' refer to in natural language processing?
Answer: Linking language model outputs to verified external facts or real-world entities
Knowledge grounding connects model-generated statements to external, verifiable sources to reduce hallucinations and improve factual reliability.
Which of the following best describes 'closed-world assumption' in knowledge systems?
Answer: Any fact not known to be true is assumed false
Under the closed-world assumption, anything not explicitly present in the knowledge base is treated as false, unlike the open-world assumption used in many semantic web systems.