Learning a Large Scale of Ontology from Japanese Wikipedia

Authors:
Susumu Tamagawa;Shinya Sakurai;Takuya Tejima;Takeshi Morita;Noriaki Izumi;Takahira Yamaguchi
Affiliations:
-;-;-;-;-;-
Venue:
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Year:
2010

Citing 0
Cited 6

Intelligent humanoid robot with japanese Wikipedia ontology and robot action ontology

Proceedings of the 6th international conference on Human-robot interaction
A mobility service based on Japanese linked data

KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part III
Extracting property semantics from japanese wikipedia

AMT'12 Proceedings of the 8th international conference on Active Media Technology
Multilingual Food and Health Ontology Learning Using Semi-Structured and Structured Web Data Sources

WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Building Up a Class Hierarchy with Properties from Japanese Wikipedia

WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Building up a class hierarchy with properties by refining and integrating Japanese Wikipedia Ontology and Japanese WordNet

Web Intelligence and Agent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Here is discussed how to learn a large scale of ontology from Japanese Wikipedia. The learned ontology includes the following properties: rdfs:subClassOf (IS-A relationships), rdf:type (class-instance relationships), owl:Object/DatatypeProperty (Infobox triples), rdfs:domain (property domains), and skos:altLabel (synonyms). Experimental case studies show us that the learned Japanese Wikipedia Ontology goes better than already existing general linguistic ontologies, such as EDR and Japanese WordNet, from the points of building costs and structure information richness.