Hamming distance
E488672
Hamming distance is a measure in information theory and computer science that counts the number of positions at which corresponding symbols in two equal-length strings differ, widely used in error detection and coding theory.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Hamming distance canonical | 3 |
| Hamming space | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T5036919 — 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: Hamming distance Context triple: [Richard W. Hamming, notableWork, Hamming distance]
-
A.
Levenstein
Levenstein is a surname, often a variant of Löwenstein, borne by individuals of German or Ashkenazi Jewish origin.
-
B.
Bhattacharyya distance
Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
-
C.
Kolmogorov distance
Kolmogorov distance is a statistical metric that measures the maximum difference between two cumulative distribution functions, commonly used to quantify convergence in distribution and in goodness-of-fit tests.
-
D.
Hellinger distance
Hellinger distance is a statistical measure of dissimilarity between probability distributions, derived from the Euclidean distance between their square-root densities and widely used in probability theory and information geometry.
-
E.
Difference Engine
The Difference Engine is an early mechanical calculator designed by Charles Babbage to automatically compute and tabulate polynomial functions, often regarded as a precursor to modern computers.
- 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: Hamming distance Target entity description: Hamming distance is a measure in information theory and computer science that counts the number of positions at which corresponding symbols in two equal-length strings differ, widely used in error detection and coding theory.
-
A.
Levenstein
Levenstein is a surname, often a variant of Löwenstein, borne by individuals of German or Ashkenazi Jewish origin.
-
B.
Bhattacharyya distance
Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
-
C.
Kolmogorov distance
Kolmogorov distance is a statistical metric that measures the maximum difference between two cumulative distribution functions, commonly used to quantify convergence in distribution and in goodness-of-fit tests.
-
D.
Hellinger distance
Hellinger distance is a statistical measure of dissimilarity between probability distributions, derived from the Euclidean distance between their square-root densities and widely used in probability theory and information geometry.
-
E.
Difference Engine
The Difference Engine is an early mechanical calculator designed by Charles Babbage to automatically compute and tabulate polynomial functions, often regarded as a precursor to modern computers.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
concept in computer science
ⓘ
concept in information theory ⓘ distance measure ⓘ metric ⓘ |
| appliesTo |
error-correcting codes
ⓘ
fixed-length codewords ⓘ |
| belongsTo | discrete metrics ⓘ |
| characterizedBy | counting symbol mismatches position-wise ⓘ |
| computationalComplexity | O(n) in string length ⓘ |
| contrastWith |
Euclidean distance
ⓘ
Manhattan distance ⓘ |
| definition | number of positions at which corresponding symbols of two equal-length strings are different ⓘ |
| domain |
binary strings
ⓘ
codewords in a block code ⓘ strings over a finite alphabet ⓘ |
| field |
coding theory
ⓘ
computer science ⓘ information theory ⓘ |
| generalizationOf | bitwise XOR difference count for binary strings ⓘ |
| mathematicalFormulation | d(x,y) = |{i : x_i ≠ y_i}| ⓘ |
| maximumValueCondition | equals string length when all positions differ ⓘ |
| metricProperty |
identity of indiscernibles
ⓘ
non-negativity ⓘ symmetry ⓘ triangle inequality ⓘ |
| minimumValueCondition | 0 when strings are identical ⓘ |
| namedAfter |
Richard Hamming
ⓘ
linked to:
Richard W. Hamming
|
| relatedTo |
Hamming code
ⓘ
Hamming weight ⓘ Levenshtein distance ⓘ edit distance ⓘ minimum distance of a code ⓘ |
| representation | distance between vertices of a hypercube graph ⓘ |
| requires | strings of equal length ⓘ |
| usedFor |
clustering of categorical data
ⓘ
coding theory analysis ⓘ error correction ⓘ error detection ⓘ measuring difference between strings of equal length ⓘ pattern recognition ⓘ similarity search in discrete spaces ⓘ |
| usedIn |
analysis of channel capacity
ⓘ
bioinformatics for comparing sequences of equal length ⓘ data transmission reliability ⓘ design of error-correcting codes ⓘ digital communications ⓘ hashing and locality-sensitive hashing for binary vectors ⓘ |
| usedToDefine |
Hamming space
ⓘ
linked to:
Hamming distance
|
| valueRange | set of non-negative integers ⓘ |
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: Hamming distance Description of subject: Hamming distance is a measure in information theory and computer science that counts the number of positions at which corresponding symbols in two equal-length strings differ, widely used in error detection and coding theory.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Hamming distance