Presentation 2011-11-21
Machine Learning Approach to Semantic Integration of Transitive Data for Knowledge Utilization
Daisuke MORITA, Kyoji IIDUKA, Takahiko MURAYAMA, Jun-ichi AKAHANI,
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Abstract(in English) There is a growing need for utilizing accumulated organizational data as knowledge. However, identity or inheritance of organizational data tends not to be controlled despite frequent changes of attribute values such as names or IDs by the changes of terms, hi this research, we propose the method to determine whether an entity is the same as another entity whose attribute values are altered by changing times. We opt for the approach to such data integration by applying the model derived from supervised learning using surrounding information and its semantics. We show experimentally that our approach is superior to existing data integration methods toward the problem of determining the sameness of transitive data.
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Keyword(in English) Semantic Web / Machine Learning / RDF / Semantic Data Integration
Paper # AI2011-20
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Committee AI
Conference Date 2011/11/14(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Machine Learning Approach to Semantic Integration of Transitive Data for Knowledge Utilization
Sub Title (in English)
Keyword(1) Semantic Web
Keyword(2) Machine Learning
Keyword(3) RDF
Keyword(4) Semantic Data Integration
1st Author's Name Daisuke MORITA
1st Author's Affiliation NTT Information Sharing Platform Laboratories, NTT Corporation()
2nd Author's Name Kyoji IIDUKA
2nd Author's Affiliation NTT Information Sharing Platform Laboratories, NTT Corporation
3rd Author's Name Takahiko MURAYAMA
3rd Author's Affiliation NTT Information Sharing Platform Laboratories, NTT Corporation
4th Author's Name Jun-ichi AKAHANI
4th Author's Affiliation NTT Information Sharing Platform Laboratories, NTT Corporation
Date 2011-11-21
Paper # AI2011-20
Volume (vol) vol.111
Number (no) 310
Page pp.pp.-
#Pages 6
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