DataTerminology.comNEW
Organized information internalized through experience and reasoning to guide action.
DESCRIPTION
Knowledge structures and interprets information to enable action and reasoning. It can be explicit (documented) or tacit (experiential). In enterprise contexts, knowledge is modeled via ontologies, taxonomies, and knowledge graphs. Knowledge management fosters creation, sharing, and application. In DIKW, knowledge builds on information to provide understanding applicable across contexts. AI systems increasingly encode and retrieve knowledge to augment decision-making.
ALTERNATIVE DEFINITIONS
Wikipedia
Knowledge is an umbrella term for many different forms of understanding. It is usually understood as awareness of facts or as practical or theoretical understanding of a subject.
Highlights both factual awareness and practical/theoretical understanding.
Stanford Encyclopedia of Philosophy
The analysis of knowledge concerns the nature of propositional knowledge, which is knowledge of propositions, and in particular, what makes such knowledge different from mere belief.
Focuses on propositional knowledge and its distinction from belief.
RELATED TERMS
Wisdom Data Tacit Knowledge Explicit Knowledge Knowledge Representation Knowledge Graph DIKW Pyramid Epistemology Semantic Web
COMPETING TERMS
Information Belief
RELATIONS
KNOWLEDGE AREAS
Semantic & Knowledge Modeling Data Management AI Domain & Business Context
TARGET AUDIENCE
Information Analyst Data Steward Data Architect Enterprise Architect Chief Data Officer
EXTERNAL SOURCES
- Wikipedia - Knowledge
- Stanford Encyclopedia of Philosophy - Deep overview of epistemological analyses
- W3C - Standards for representing and sharing knowledge
- ISO - Knowledge management systems standard overview
TOP QUESTIONS
How does Knowledge differ from Information?
Information provides context; knowledge integrates information with experience and reasoning to guide action and decisions.
Category: Misunderstanding
What is tacit versus explicit Knowledge?
Tacit knowledge is personal, experiential, hard to codify; explicit knowledge is documented, shareable, and structured.
Category: Definition
How can organizations model Knowledge?
Use ontologies, taxonomies, and knowledge graphs to represent concepts, relationships, and rules for retrieval and reasoning.
Category: Requirements
Why is Knowledge important for AI?
Knowledge enables reasoning, reduces hallucinations, and supports explainability via structured facts and constraints.
Category: Relevancy
What tools help manage Knowledge?
Knowledge bases, semantic repositories, graph databases, and collaboration platforms facilitate capture and sharing.
Category: Platform
What are common mistakes in Knowledge management?
Over-documenting without curation, neglecting tacit sharing, and failing to align with domain language.
Category: Mistakes
How to start building organizational Knowledge?
Establish a business glossary, identify core domains, encode key concepts, and encourage communities of practice.
Category: Example
How does the DIKW Pyramid position Knowledge?
Knowledge sits above information, adding structure and interpretive frameworks that enable application.
Category: Overview
What is a major debate about Knowledge in AI?
Symbolic versus statistical approaches: encoded rules versus learned representations for managing and using knowledge.
Category: Opinion