information theory
E158223
academic discipline
branch of applied mathematics
branch of electrical engineering
field of study
mathematical theory
Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
All labels observed (4)
| Label | Occurrences |
|---|---|
| information theory canonical | 14 |
| Shannon information theory | 2 |
| Information Theory and Statistics | 1 |
| Shannon theory | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1374565 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: information theory Context triple: [Norbert Wiener, influenced, information theory]
-
A.
Shannon entropy
Shannon entropy is a fundamental measure in information theory that quantifies the average uncertainty or information content in a random variable or message source.
-
B.
An Introduction to Information Theory: Symbols, Signals and Noise
An Introduction to Information Theory: Symbols, Signals and Noise is a classic, accessible textbook that explains the fundamental concepts of information theory, communication, and coding for a broad scientific and engineering audience.
-
C.
Shannon–Khinchin axioms
The Shannon–Khinchin axioms are a set of fundamental conditions that uniquely characterize Shannon entropy as the standard measure of information and uncertainty in probability theory and information theory.
-
D.
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory is a leading peer-reviewed journal that publishes foundational and advanced research on the theory of information, coding, communication, and related mathematical disciplines.
-
E.
Rényi entropy
Rényi entropy is a generalized measure of information and uncertainty that extends Shannon entropy by introducing a tunable order parameter to emphasize different aspects of a probability distribution.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: information theory Target entity description: Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
-
A.
Shannon entropy
Shannon entropy is a fundamental measure in information theory that quantifies the average uncertainty or information content in a random variable or message source.
-
B.
An Introduction to Information Theory: Symbols, Signals and Noise
An Introduction to Information Theory: Symbols, Signals and Noise is a classic, accessible textbook that explains the fundamental concepts of information theory, communication, and coding for a broad scientific and engineering audience.
-
C.
Shannon–Khinchin axioms
The Shannon–Khinchin axioms are a set of fundamental conditions that uniquely characterize Shannon entropy as the standard measure of information and uncertainty in probability theory and information theory.
-
D.
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory is a leading peer-reviewed journal that publishes foundational and advanced research on the theory of information, coding, communication, and related mathematical disciplines.
-
E.
Rényi entropy
Rényi entropy is a generalized measure of information and uncertainty that extends Shannon entropy by introducing a tunable order parameter to emphasize different aspects of a probability distribution.
- F. None of above. chosen
Statements (59)
| Predicate | Object |
|---|---|
| instanceOf |
academic discipline
ⓘ
branch of applied mathematics ⓘ branch of electrical engineering ⓘ field of study ⓘ mathematical theory ⓘ |
| appliesTo |
bioinformatics
ⓘ
control theory ⓘ cryptography ⓘ data compression algorithms ⓘ digital communication systems ⓘ error-correcting codes ⓘ machine learning ⓘ neuroscience ⓘ statistical physics ⓘ |
| basedOn |
combinatorics
ⓘ
probability theory ⓘ statistics ⓘ |
| developedBy | Claude Shannon ⓘ |
| focusesOn |
communication
ⓘ
data compression ⓘ error control ⓘ quantification of information ⓘ signal processing ⓘ |
| foundationYear | 1948 ⓘ |
| hasApplicationIn |
lossless compression
ⓘ
lossy compression ⓘ storage systems ⓘ wireless communication ⓘ |
| hasCoreConcept |
Fano inequality
ⓘ
Kullback–Leibler divergence ⓘ Shannon entropy ⓘ channel coding theorem ⓘ data processing inequality ⓘ noisy-channel coding theorem ⓘ relative entropy ⓘ source coding theorem ⓘ typical set ⓘ |
| hasKeyPublication | A Mathematical Theory of Communication ⓘ |
| hasSubfield |
algorithmic information theory
ⓘ
channel coding ⓘ coding theory ⓘ network information theory ⓘ quantum information theory ⓘ rate–distortion theory ⓘ source coding ⓘ |
| historicalOrigin |
Bell Telephone Laboratories
ⓘ
surface form:
Bell Labs
|
| introducedConcept |
channel capacity
ⓘ
data compression limit ⓘ information entropy ⓘ mutual information ⓘ rate–distortion function ⓘ typical sequences ⓘ |
| relatedTo |
computer science
ⓘ
signal processing ⓘ statistical mechanics ⓘ thermodynamics ⓘ |
| usesUnit |
bit
ⓘ
nat ⓘ Shannon entropy ⓘ
surface form:
shannon
|
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: information theory Description of subject: Information theory is a mathematical framework for quantifying information, communication, and data compression, foundational to modern digital communication and signal processing.
Referenced by (18)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Information Theory and Statistics
subject surface form:
Markov process
subject surface form:
Kullback–Leibler divergence
this entity surface form:
Shannon theory
subject surface form:
Paul Baran
this entity surface form:
Shannon information theory