PageRank algorithm
E66951
The PageRank algorithm is a link analysis method used by search engines, notably Google, to rank web pages in search results based on their importance within the web’s link structure.
All labels observed (5)
| Label | Occurrences |
|---|---|
| PageRank | 5 |
| PageRank algorithm canonical | 4 |
| Personalized PageRank | 1 |
| Topic-sensitive PageRank | 1 |
| Weighted PageRank | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T535591 — 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: PageRank algorithm Context triple: [Larry Page, knownFor, PageRank algorithm]
-
A.
Alexa Internet
Alexa Internet was a web traffic analysis and ranking company best known for providing website popularity metrics and analytics services before its shutdown in 2022.
-
B.
World Wide Web
The World Wide Web is a global system of interlinked hypertext documents and resources accessed via the internet, enabling users worldwide to browse, share, and interact with information through web browsers.
-
C.
AltaVista
AltaVista was one of the earliest and most popular web search engines of the 1990s, known for its fast, comprehensive internet search before being eclipsed by later competitors.
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D.
Finding: Publishing and Linking on the Web
"Finding: Publishing and Linking on the Web" is a W3C Technical Architecture Group document that provides best-practice guidance on how web resources should be published and linked to ensure durability, interoperability, and clarity on the Web.
-
E.
Finding: The Self-Describing Web
"Finding: The Self-Describing Web" is a W3C Technical Architecture Group document that explains how web resources should carry or link to enough metadata and semantics to allow automated agents and humans to understand and use them without prior agreement.
- 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: PageRank algorithm Target entity description: The PageRank algorithm is a link analysis method used by search engines, notably Google, to rank web pages in search results based on their importance within the web’s link structure.
-
A.
Alexa Internet
Alexa Internet was a web traffic analysis and ranking company best known for providing website popularity metrics and analytics services before its shutdown in 2022.
-
B.
World Wide Web
The World Wide Web is a global system of interlinked hypertext documents and resources accessed via the internet, enabling users worldwide to browse, share, and interact with information through web browsers.
-
C.
AltaVista
AltaVista was one of the earliest and most popular web search engines of the 1990s, known for its fast, comprehensive internet search before being eclipsed by later competitors.
-
D.
Finding: Publishing and Linking on the Web
"Finding: Publishing and Linking on the Web" is a W3C Technical Architecture Group document that provides best-practice guidance on how web resources should be published and linked to ensure durability, interoperability, and clarity on the Web.
-
E.
Finding: The Self-Describing Web
"Finding: The Self-Describing Web" is a W3C Technical Architecture Group document that explains how web resources should carry or link to enough metadata and semantics to allow automated agents and humans to understand and use them without prior agreement.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
graph algorithm
ⓘ
link analysis algorithm ⓘ ranking algorithm ⓘ |
| alsoUsedFor |
ranking scientific papers
ⓘ
ranking social network nodes ⓘ recommendation systems ⓘ spam detection ⓘ |
| basedOn |
Markov processes
ⓘ
surface form:
Markov chain
eigenvector centrality ⓘ random surfer model ⓘ |
| category |
Google
ⓘ
surface form:
Google technologies
search engine optimization ⓘ |
| complexity | iterative polynomial-time algorithm ⓘ |
| computationalMethod | power iteration ⓘ |
| coreIdea | a page is important if important pages link to it ⓘ |
| describedInPaper |
The Anatomy of a Large-Scale Hypertextual Web Search Engine
ⓘ
The Anatomy of a Large-Scale Hypertextual Web Search Engine ⓘ
surface form:
The PageRank Citation Ranking: Bringing Order to the Web
|
| developedAt | Stanford University ⓘ |
| developedBy |
Larry Page
ⓘ
Sergey Brin ⓘ |
| field |
information retrieval
ⓘ
network science ⓘ web search ⓘ |
| handlesIssue |
dangling nodes
ⓘ
rank sinks ⓘ |
| hasVariant |
PageRank algorithm
self-linksurface differs
ⓘ
surface form:
Personalized PageRank
PageRank algorithm self-linksurface differs ⓘ
surface form:
Topic-sensitive PageRank
PageRank algorithm self-linksurface differs ⓘ
surface form:
Weighted PageRank
|
| influenced |
graph-based ranking methods
ⓘ
modern search ranking algorithms ⓘ |
| input | hyperlinks between web pages ⓘ |
| introducedIn | 1998 ⓘ |
| licenseHistory | patented by Stanford University ⓘ |
| mathematicalFormulation | eigenvector of normalized link matrix ⓘ |
| operatesOn |
directed graph
ⓘ
web graph ⓘ |
| output | importance score for each page ⓘ |
| patentAssignee | Stanford University ⓘ |
| patentLicensedTo | Google ⓘ |
| primaryUse | ranking web pages in search results ⓘ |
| relatedConcept |
HITS algorithm
ⓘ
centrality measures in networks ⓘ |
| scale | web-scale graphs ⓘ |
| typicalDampingFactor | 0.85 ⓘ |
| usedBy |
Google Search
ⓘ
surface form:
Google search engine
|
| usesConcept |
link analysis
ⓘ
probability distribution over pages ⓘ stationary distribution of a Markov chain ⓘ |
| usesParameter | damping factor ⓘ |
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: PageRank algorithm Description of subject: The PageRank algorithm is a link analysis method used by search engines, notably Google, to rank web pages in search results based on their importance within the web’s link structure.
Referenced by (12)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
PageRank
subject surface form:
PageRank
this entity surface form:
Personalized PageRank
subject surface form:
PageRank
this entity surface form:
Topic-sensitive PageRank
subject surface form:
PageRank
this entity surface form:
Weighted PageRank
this entity surface form:
PageRank
this entity surface form:
PageRank
this entity surface form:
PageRank
this entity surface form:
PageRank