Susumu Tamagawa

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Susumu Tamagawa is an author.

Publications

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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
An automatic sameAs link discovery from Wikipedia Disambiguation
Ontology
SameAs link
Spelling variants
Synonym
Wikipedia
Lecture Notes in Computer Science English 2014 Spelling variants of words or word sense ambiguity takes many costs in such processes as Data Integration, Information Searching, data pre-processing for Data Mining, and so on. It is useful to construct relations between a word or phrases and a representative name of the entity to meet these demands. To reduce the costs, this paper discusses how to automatically discover "sameAs" and "meaningOf" links from Japanese Wikipedia. In order to do so, we gathered relevant features such as IDF, string similarity, number of hypernym, and so on. We have identified the link-based score on salient features based on SVM results with 960,000 anchor link pairs. These case studies show us that our link discovery method goes well with more than 70% precision/ recall rate. 0 0
Building up a class hierarchy with properties from Japanese Wikipedia Ontology
Ontology learning
Wikipedia
Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 English 2012 Japanese Wikipedia Ontology, which we have constructed semi-automatically from Japanese Wikipedia, has problems of lacking upper classes and appropriate definition of properties. The purpose of our research is to complement the upper classes in Japanese Wikipedia Ontology and build up a class hierarchy with properties by integrating Japanese Wikipedia Ontology and Japanese Word Net. In this paper, we propose a method to build up the class hierarchy with properties by lifting up common properties that are defined in sibling classes to more upper classes in Japanese Wikipedia Ontology. We also introduce an attempt to integrate Japanese Wikipedia Ontology and Japanese Word Net. 0 0
Extracting property semantics from Japanese Wikipedia Ontology
Ontology learning
Property definition
Wikipedia
Lecture Notes in Computer Science English 2012 Here is discussed how to build up ontology with many properties from Japanese Wikipedia. The ontology includes is-a relationship (rdfs:subClassOf), class-instance relationship (rdf:type) and synonym relation (skos:altLabel) moreover it includes property relations and types. Property relations are triples, property domain (rdfs:domain) and property range (rdfs:range). Property types are object (owl:ObjectProperty), data (owl:DatatypeProperty), symmetric (owl:SymmetricProperty), transitive (owl:TransitiveProperty), functional (owl:FunctionalProperty) and inverse functional (owl:InverseFunctionalProperty). 0 0
Intelligent humanoid robot with Japanese Wikipedia Ontology and Robot Action Ontology Action
Dialogue
Japanese speech interface
Japanese Wikipedia Ontology
Robot action ontology
Semantic web
HRI 2011 - Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction English 2011 WioNA (Wikipedia Ontology NAo) is proposed to build much better HRI by integrating four elements: Japanese speech interface, semantic interpretation, Japanese Wikipedia Ontology and Robot Action Ontology. WioNA is implemented on a humanoid robot "Nao". In WioNA, we developed two ontologies: Japanese Wikipedia Ontology and Robot Action Ontology. Japanese Wikipedia Ontology has a large size of concept hierarchy and instance network with many properties from Japanese Wikipedia (semi) automatically. By giving Japanese Wikipedia Ontology to Nao as wisdom, Nao can dialogue with users on many topics of various fields. Robot Action Ontology, in contrast, is built by organizing various performable actions of Nao to control and generate robot actions. Aligning Robot Action Ontology with Japanese Wikipedia Ontology enables Nao to perform related actions to dialogue topics. To show the validities of WioNA, we describe human-robot conversation logs of two case studies whose dialogue topics are sport and rock singer. These case studies show us how HRI goes well in WioNA with these topics. Copyright 2011 ACM. 0 0
Intelligent humanoid robot with japanese Wikipedia ontology and robot action ontology International Conference on Human-robot Interaction English 2011 0 0
Human Robot Interaction Based on Wikipedia Ontology and Robot Action Ontology International Joint Conference on Knowledge-Based Software Engineering English 2010 0 0
Learning a Large Scale of Ontology from Japanese Wikipedia Transactions of the Japanese Society for Artificial Intelligence English 2010 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 relationship), rdf:type (class-instance relationship), owl:Object/DatatypeProperty (Infobox triple), rdfs:domain (property domain), and skos:altLabel (synonym). 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. 0 0
Learning a large scale of ontology from Japanese Wikipedia Ontology
Ontology learning
Wikipedia
Transactions of the Japanese Society for Artificial Intelligence Japanese 2010 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 relationship), rdf:type (class-instance relationship), owl:Object/DatatypeProperty (Infobox triple), rdfs:domain (property domain), and skos:altLabel (synonym). 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. 0 0