Difference between revisions of "Generating Information-Rich Taxonomy from Wikipedia"

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== Overview ==
 
== Overview ==
 
Even though hyponymy relation acquisition has been extensively studied, “how informative such acquired hyponymy relations are” has not been sufficiently discussed. Authors found that the hypernyms in automatically acquired hyponymy relations were often too vague or ambiguous to specify the meaning of their hyponyms. For instance, hypernym work is vague and ambiguous in hyponymy relations work/Avatar and work/The Catcher in the Rye. In this paper, authors propose a simple method of generating intermediate concepts of hyponymy relations that can make such (vague) hypernyms more specific. Authors method generates such an information-rich hyponymy relation as work / work by film director / work by James Cameron / Avatar from the less informative relation work/Avatar. Furthermore, the generated relation work by film director/Avatar can be paraphrased into a new relation movie/Avatar. Experiments showed that method successfully acquired 2,719,441 enriched hyponymy relations with one intermediate concept with 0.853 precision and another 6,347,472 hyponymy relations with 0.786 precision.
 
Even though hyponymy relation acquisition has been extensively studied, “how informative such acquired hyponymy relations are” has not been sufficiently discussed. Authors found that the hypernyms in automatically acquired hyponymy relations were often too vague or ambiguous to specify the meaning of their hyponyms. For instance, hypernym work is vague and ambiguous in hyponymy relations work/Avatar and work/The Catcher in the Rye. In this paper, authors propose a simple method of generating intermediate concepts of hyponymy relations that can make such (vague) hypernyms more specific. Authors method generates such an information-rich hyponymy relation as work / work by film director / work by James Cameron / Avatar from the less informative relation work/Avatar. Furthermore, the generated relation work by film director/Avatar can be paraphrased into a new relation movie/Avatar. Experiments showed that method successfully acquired 2,719,441 enriched hyponymy relations with one intermediate concept with 0.853 precision and another 6,347,472 hyponymy relations with 0.786 precision.
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=== Wikipedia Quality ===
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Yamada, Ichiro; Hashimoto, Chikara; Oh, Jong-Hoon; Torisawa, Kentaro; Kuroda, Kow; Saeger, Stijn De; Tsuchida, Masaaki; Kazama, Jun’ichi. (2010). "[[Generating Information-Rich Taxonomy from Wikipedia]]".DOI: 10.1109/IUCS.2010.5666764.
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=== English Wikipedia ===
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{{cite journal |last1=Yamada |first1=Ichiro |last2=Hashimoto |first2=Chikara |last3=Oh |first3=Jong-Hoon |last4=Torisawa |first4=Kentaro |last5=Kuroda |first5=Kow |last6=Saeger |first6=Stijn De |last7=Tsuchida |first7=Masaaki |last8=Kazama |first8=Jun’ichi |title=Generating Information-Rich Taxonomy from Wikipedia |date=2010 |doi=10.1109/IUCS.2010.5666764 |url=https://wikipediaquality.com/wiki/Generating_Information-Rich_Taxonomy_from_Wikipedia}}
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Yamada, Ichiro; Hashimoto, Chikara; Oh, Jong-Hoon; Torisawa, Kentaro; Kuroda, Kow; Saeger, Stijn De; Tsuchida, Masaaki; Kazama, Jun’ichi. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Generating_Information-Rich_Taxonomy_from_Wikipedia">Generating Information-Rich Taxonomy from Wikipedia</a>&amp;quot;.DOI: 10.1109/IUCS.2010.5666764.
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Revision as of 11:00, 13 December 2019


Generating Information-Rich Taxonomy from Wikipedia
Authors
Ichiro Yamada
Chikara Hashimoto
Jong-Hoon Oh
Kentaro Torisawa
Kow Kuroda
Stijn De Saeger
Masaaki Tsuchida
Jun’ichi Kazama
Publication date
2010
DOI
10.1109/IUCS.2010.5666764
Links
Original

Generating Information-Rich Taxonomy from Wikipedia - scientific work related to Wikipedia quality published in 2010, written by Ichiro Yamada, Chikara Hashimoto, Jong-Hoon Oh, Kentaro Torisawa, Kow Kuroda, Stijn De Saeger, Masaaki Tsuchida and Jun’ichi Kazama.

Overview

Even though hyponymy relation acquisition has been extensively studied, “how informative such acquired hyponymy relations are” has not been sufficiently discussed. Authors found that the hypernyms in automatically acquired hyponymy relations were often too vague or ambiguous to specify the meaning of their hyponyms. For instance, hypernym work is vague and ambiguous in hyponymy relations work/Avatar and work/The Catcher in the Rye. In this paper, authors propose a simple method of generating intermediate concepts of hyponymy relations that can make such (vague) hypernyms more specific. Authors method generates such an information-rich hyponymy relation as work / work by film director / work by James Cameron / Avatar from the less informative relation work/Avatar. Furthermore, the generated relation work by film director/Avatar can be paraphrased into a new relation movie/Avatar. Experiments showed that method successfully acquired 2,719,441 enriched hyponymy relations with one intermediate concept with 0.853 precision and another 6,347,472 hyponymy relations with 0.786 precision.

Embed

Wikipedia Quality

Yamada, Ichiro; Hashimoto, Chikara; Oh, Jong-Hoon; Torisawa, Kentaro; Kuroda, Kow; Saeger, Stijn De; Tsuchida, Masaaki; Kazama, Jun’ichi. (2010). "[[Generating Information-Rich Taxonomy from Wikipedia]]".DOI: 10.1109/IUCS.2010.5666764.

English Wikipedia

{{cite journal |last1=Yamada |first1=Ichiro |last2=Hashimoto |first2=Chikara |last3=Oh |first3=Jong-Hoon |last4=Torisawa |first4=Kentaro |last5=Kuroda |first5=Kow |last6=Saeger |first6=Stijn De |last7=Tsuchida |first7=Masaaki |last8=Kazama |first8=Jun’ichi |title=Generating Information-Rich Taxonomy from Wikipedia |date=2010 |doi=10.1109/IUCS.2010.5666764 |url=https://wikipediaquality.com/wiki/Generating_Information-Rich_Taxonomy_from_Wikipedia}}

HTML

Yamada, Ichiro; Hashimoto, Chikara; Oh, Jong-Hoon; Torisawa, Kentaro; Kuroda, Kow; Saeger, Stijn De; Tsuchida, Masaaki; Kazama, Jun’ichi. (2010). &quot;<a href="https://wikipediaquality.com/wiki/Generating_Information-Rich_Taxonomy_from_Wikipedia">Generating Information-Rich Taxonomy from Wikipedia</a>&quot;.DOI: 10.1109/IUCS.2010.5666764.