Rabin–Karp algorithm
E439883
The Rabin–Karp algorithm is a string-searching technique that uses hashing to efficiently find any one of a set of pattern strings in a text.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Rabin–Karp algorithm canonical | 3 |
| Rabin–Karp string search algorithm | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4416409 — 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: Rabin–Karp algorithm Context triple: [Knuth–Morris–Pratt algorithm, comparedWith, Rabin–Karp algorithm]
-
A.
Knuth–Morris–Pratt algorithm
The Knuth–Morris–Pratt algorithm is a classic linear-time string-searching algorithm that efficiently finds occurrences of a pattern within a text by precomputing a prefix function to avoid redundant comparisons.
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B.
Boyer–Moore string-search algorithm
The Boyer–Moore string-search algorithm is a highly efficient pattern-matching algorithm that scans text from right to left and uses precomputed shift rules to skip sections of the text, making it one of the fastest practical algorithms for substring search.
-
C.
Thompson's algorithm for regular expression matching
Thompson's algorithm for regular expression matching is a classic method that converts regular expressions into nondeterministic finite automata (NFAs) to enable efficient pattern matching in text processing.
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D.
Marzullo's algorithm
Marzullo's algorithm is a method for selecting the most likely correct time interval from multiple, possibly conflicting time sources, commonly used in clock synchronization systems.
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E.
Berlekamp–Massey algorithm
The Berlekamp–Massey algorithm is a key algorithm in coding theory and cryptography used to efficiently determine the shortest linear feedback shift register that generates a given binary sequence.
- 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: Rabin–Karp algorithm Target entity description: The Rabin–Karp algorithm is a string-searching technique that uses hashing to efficiently find any one of a set of pattern strings in a text.
-
A.
Knuth–Morris–Pratt algorithm
The Knuth–Morris–Pratt algorithm is a classic linear-time string-searching algorithm that efficiently finds occurrences of a pattern within a text by precomputing a prefix function to avoid redundant comparisons.
-
B.
Boyer–Moore string-search algorithm
The Boyer–Moore string-search algorithm is a highly efficient pattern-matching algorithm that scans text from right to left and uses precomputed shift rules to skip sections of the text, making it one of the fastest practical algorithms for substring search.
-
C.
Thompson's algorithm for regular expression matching
Thompson's algorithm for regular expression matching is a classic method that converts regular expressions into nondeterministic finite automata (NFAs) to enable efficient pattern matching in text processing.
-
D.
Marzullo's algorithm
Marzullo's algorithm is a method for selecting the most likely correct time interval from multiple, possibly conflicting time sources, commonly used in clock synchronization systems.
-
E.
Berlekamp–Massey algorithm
The Berlekamp–Massey algorithm is a key algorithm in coding theory and cryptography used to efficiently determine the shortest linear feedback shift register that generates a given binary sequence.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
computer science concept
ⓘ
pattern-matching algorithm ⓘ string-search algorithm ⓘ |
| advantage |
efficient for multiple-pattern search
ⓘ
good for plagiarism detection ⓘ good for searching for many patterns simultaneously ⓘ simple to implement ⓘ |
| basedOn | rolling hash ⓘ |
| canBe |
deterministic
ⓘ
randomized ⓘ |
| canFind |
multiple patterns in text
ⓘ
single pattern in text ⓘ |
| category |
randomized algorithms
ⓘ
string algorithms ⓘ |
| comparesWith |
Aho–Corasick algorithm
ⓘ
Boyer–Moore algorithm ⓘ
linked to:
Boyer–Moore string-search algorithm
Knuth–Morris–Pratt algorithm ⓘ |
| designedFor |
pattern matching in text
ⓘ
string searching ⓘ |
| disadvantage |
hash collisions can cause extra comparisons
ⓘ
worst-case time can degrade to O(nm) ⓘ |
| goal |
efficient multi-pattern search
ⓘ
efficient substring search ⓘ |
| hashCollisionProbability | can be made low with large modulus ⓘ |
| hashFunctionType | polynomial rolling hash ⓘ |
| inputParameter |
alphabet size
ⓘ
modulus for hashing ⓘ |
| introducedBy |
Michael O. Rabin
ⓘ
linked to:
Michael Rabin
Richard M. Karp ⓘ
linked to:
Richard Karp
|
| keyIdea |
compare hash values instead of characters initially
ⓘ
update hash in constant time when sliding window ⓘ use modular arithmetic for hash computation ⓘ |
| operatesOn |
pattern string
ⓘ
text string ⓘ |
| originalPaperTitle | Efficient randomized pattern-matching algorithms ⓘ |
| publicationYear | 1987 ⓘ |
| publishedIn | Communications of the ACM ⓘ |
| requires | verification of potential matches by direct comparison ⓘ |
| spaceComplexity | O(1) ⓘ |
| timeComplexityAverageCase | O(n + m) ⓘ |
| timeComplexityWorstCase | O(nm) ⓘ |
| typicalUseCase | searching for a pattern in a large text ⓘ |
| usedIn |
information retrieval
ⓘ
intrusion detection systems ⓘ plagiarism detection systems ⓘ search utilities ⓘ text editors ⓘ |
| uses | hashing ⓘ |
| verificationStep | character-by-character comparison on hash match ⓘ |
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: Rabin–Karp algorithm Description of subject: The Rabin–Karp algorithm is a string-searching technique that uses hashing to efficiently find any one of a set of pattern strings in a text.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Rabin–Karp algorithm