The Anatomy of a Large-Scale Hypertextual Web Search Engine
E68398
"The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
All labels observed (2)
| Label | Occurrences |
|---|---|
| The Anatomy of a Large-Scale Hypertextual Web Search Engine canonical | 3 |
| The PageRank Citation Ranking: Bringing Order to the Web | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T542268 — 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: The Anatomy of a Large-Scale Hypertextual Web Search Engine Context triple: [Sergey Brin, notableWork, The Anatomy of a Large-Scale Hypertextual Web Search Engine]
-
A.
PageRank algorithm
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.
-
B.
Architecture of the World Wide Web, Volume One
Architecture of the World Wide Web, Volume One is a W3C-authored technical document that defines the foundational principles and design of the Web’s architecture.
-
C.
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.
-
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.
ACM Transactions on the Web
ACM Transactions on the Web is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research and developments related to web technologies and applications.
- 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: The Anatomy of a Large-Scale Hypertextual Web Search Engine Target entity description: "The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
-
A.
PageRank algorithm
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.
-
B.
Architecture of the World Wide Web, Volume One
Architecture of the World Wide Web, Volume One is a W3C-authored technical document that defines the foundational principles and design of the Web’s architecture.
-
C.
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.
-
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.
ACM Transactions on the Web
ACM Transactions on the Web is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research and developments related to web technologies and applications.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
computer science paper
ⓘ
research paper ⓘ scientific article ⓘ |
| affiliatedWith | Google ⓘ |
| author |
Larry Page
ⓘ
Sergey Brin ⓘ |
| citedFor |
architecture of Google
ⓘ
introduction of PageRank ⓘ use of link analysis in ranking ⓘ |
| describes |
Google crawler
ⓘ
Google indexer ⓘ Google Search ⓘ
surface form:
Google query processor
Google system architecture ⓘ early Google prototype ⓘ handling of queries ⓘ storage of web documents ⓘ use of commodity hardware for search ⓘ |
| field |
computer science
ⓘ
data mining ⓘ hypertext analysis ⓘ information retrieval ⓘ web search ⓘ |
| focusesOn |
efficiency of indexing
ⓘ
quality of search results ⓘ scalability of web search ⓘ |
| goal |
handle large-scale web data
ⓘ
improve quality of web search ⓘ |
| hasImpactOn |
commercial search engines
ⓘ
search engine design ⓘ web information retrieval research ⓘ |
| institution | Stanford University ⓘ |
| introduces |
PageRank algorithm
ⓘ
surface form:
PageRank
|
| language | English ⓘ |
| mainTopic |
Google search engine
ⓘ
PageRank algorithm ⓘ indexing of web pages ⓘ large-scale web search architecture ⓘ link analysis ⓘ ranking of search results ⓘ web crawling ⓘ |
| proposes |
anchor text as a signal for relevance
ⓘ
using hyperlinks as votes ⓘ using link structure for ranking ⓘ |
| publicationYear | 1998 ⓘ |
| publishedAtEvent | WWW7 ⓘ |
| publishedIn | Proceedings of the Seventh International World Wide Web Conference ⓘ |
| publisher | International World Wide Web Conference Committee ⓘ |
| url | http://infolab.stanford.edu/~backrub/google.html ⓘ |
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: The Anatomy of a Large-Scale Hypertextual Web Search Engine Description of subject: "The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
PageRank algorithm
→
describedInPaper
→
The Anatomy of a Large-Scale Hypertextual Web Search Engine
ⓘ
subject surface form:
PageRank
PageRank algorithm
→
describedInPaper
→
The Anatomy of a Large-Scale Hypertextual Web Search Engine
ⓘ
subject surface form:
PageRank
this entity surface form:
The PageRank Citation Ranking: Bringing Order to the Web
subject surface form:
Sergey Brin