Presentation 1994/5/13
Self-refining method for large-scale knowledge base of words
Kaname Kasahara, Takashi Yukawa, Kazumitsu Matsuzawa, Tsutomu Ishikawa,
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Abstract(in English) It is difficult to manually build large-scale knowledge bases of words.therefore,we tried automatic construction of a 40,000-word knowledge base,and proved that similarity between the words can be judged effectively using that knowledge base.However,more attributes of words are needed to judge this similarity more accurately. This paper proposes self-refining methods for the knowledge base, that is acquiring missing attributes of words from other parts of the knowledge base.The first method is to refer to the attributes of words that are contained in the original words list of attributes.The other is a method for acquiring new attributes from a tree-structed thesaurus.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) magnetic recording / recording media / recording noise / magnetic transition / readback waveform
Paper # DE94-1
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Committee DE
Conference Date 1994/5/13(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Self-refining method for large-scale knowledge base of words
Sub Title (in English)
Keyword(1) magnetic recording
Keyword(2) recording media
Keyword(3) recording noise
Keyword(4) magnetic transition
Keyword(5) readback waveform
1st Author's Name Kaname Kasahara
1st Author's Affiliation NTT Communication Science Laboratories()
2nd Author's Name Takashi Yukawa
2nd Author's Affiliation NTT Communication Science Laboratories
3rd Author's Name Kazumitsu Matsuzawa
3rd Author's Affiliation NTT Communication Science Laboratories
4th Author's Name Tsutomu Ishikawa
4th Author's Affiliation NTT Communication Science Laboratories
Date 1994/5/13
Paper # DE94-1
Volume (vol) vol.94
Number (no) 33
Page pp.pp.-
#Pages 8
Date of Issue