Self-emergence of knowledge trees: extraction of the Wikipedia hierarchies

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Self-emergence of knowledge trees: extraction of the Wikipedia hierarchies
Authors: Lev Muchnik, Royi Itzhack, Sorin Solomon, Yoram Louzoun [edit item]
Citation: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 76 (1): . 2007.
Publication type: Journal article
Peer-reviewed: Yes
Database(s):
DOI: 10.1103/PhysRevE.76.016106.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Self-emergence of knowledge trees: extraction of the Wikipedia hierarchies is a publication by Lev Muchnik, Royi Itzhack, Sorin Solomon, Yoram Louzoun.


[edit] Abstract

The rapid accumulation of knowledge and the recent emergence of new dynamic and practically unmoderated information repositories have rendered the classical concept of the hierarchal knowledge structure irrelevant and impossible to impose manually. This led to modern methods of data location, such as browsing or searching, which conceal the underlying information structure. We here propose methods designed to automatically construct a hierarchy from a network of related terms. We apply these methods to Wikipedia and compare the hierarchy obtained from the article network to the complementary acyclic category layer of the Wikipedia and show an excellent fit. We verify our methods in two networks with no a priori hierarchy (the E. Coli genetic regulatory network and the C. Elegans neural network) and a network of function libraries of modern computer operating systems that are intrinsically hierarchical and reproduce a known functional order.

[edit] Research questions

"We here propose methods designed to automatically construct a hierarchy from a network of related terms. We apply these methods to Wikipedia and compare the hierarchy obtained from the article network to the complementary acyclic category layer of the Wikipedia and show an excellent fit."

Research details

Topics: Ontology building [edit item]
Domains: Computer science, Library science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Case [edit item]
Theories: "We base our approach on the assumption that the hierarchical position of concepts is not only a function of their content, but also of their context France is a subcategory of state, only because there are many others like it . While the content cannot reveal the full context, context may contain information about the content. We here show that the context information is enough to extract an approximate hierarchy, ignoring the content of each term by itself." [edit item]
Research design: Statistical analysis [edit item]
Data source: Websites [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Dump [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: Multiple [edit item]

[edit] Conclusion

"We apply these methods to Wikipedia and compare the hierarchy obtained from the article network to the complementary acyclic category layer of the Wikipedia and show an excellent fit."

[edit] Comments

"Websites: Search Engines"


Further notes[edit]

Facts about "Self-emergence of knowledge trees: extraction of the Wikipedia hierarchies"RDF feed
AbstractThe rapid accumulation of knowledge and thThe rapid accumulation of knowledge and the recent emergence of new dynamic and practically unmoderated information repositories have rendered the classical concept of the hierarchal knowledge structure irrelevant and impossible to impose manually. This led to modern methods of data location, such as browsing or searching, which conceal the underlying information structure. We here propose methods designed to automatically construct a hierarchy from a network of related terms. We apply these methods to Wikipedia and compare the hierarchy obtained from the article network to the complementary acyclic category layer of the Wikipedia and show an excellent fit. We verify our methods in two networks with no a priori hierarchy (the E. Coli genetic regulatory network and the C. Elegans neural network) and a network of function libraries of modern computer operating systems that are intrinsically hierarchical and reproduce a known functional order.al and reproduce a known functional order.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
CommentsWebsites: Search Engines
ConclusionWe apply these methods to Wikipedia and compare the hierarchy obtained from the article network to the complementary acyclic category layer of the Wikipedia and show an excellent fit.
Data sourceWebsites +
Doi10.1103/PhysRevE.76.016106 +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Self-emergence%2Bof%2Bknowledge%2Btrees%3A%2Bextraction%2Bof%2Bthe%2BWikipedia%2Bhierarchies%22 +
Has authorLev Muchnik +, Royi Itzhack +, Sorin Solomon + and Yoram Louzoun +
Has domainComputer science + and Library science +
Has topicOntology building +
Issue1 +
Peer reviewedYes +
Publication typeJournal article +
Published inPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics +
Research designStatistical analysis +
Research questionsWe here propose methods designed to automaWe here propose methods designed to automatically construct a hierarchy from a network of related terms. We apply these methods to Wikipedia and compare the hierarchy obtained from the article network to the complementary acyclic category layer of the Wikipedia and show an excellent fit.f the Wikipedia and show an excellent fit.
Revid10,939 +
TheoriesWe base our approach on the assumption thaWe base our approach on the assumption that the hierarchical position of concepts is not only a function of their content, but also of their context France is a subcategory of state, only because there are many others like it . While the content cannot reveal the full context, context may contain information about the content. We here show that the context information is enough to extract an approximate hierarchy, ignoring the content of each term by itself.noring the content of each term by itself.
Theory typeDesign and action +
TitleSelf-emergence of knowledge trees: extraction of the Wikipedia hierarchies
Unit of analysisArticle +
Urlhttp://dx.doi.org/10.1103/PhysRevE.76.016106 +
Volume76 +
Wikipedia coverageCase +
Wikipedia data extractionDump +
Wikipedia languageMultiple +
Wikipedia page typeArticle +
Year2007 +