Difference between revisions of "Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology"
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+ | {{Infobox work | ||
+ | | title = Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology | ||
+ | | date = 2008 | ||
+ | | authors = [[Kotaro Nakayama]]<br />[[Takahiro Hara]]<br />[[Shojiro Nishio]] | ||
+ | | link = http://ceur-ws.org/Vol-334/paper-05.pdf | ||
+ | }} | ||
'''Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]]. | '''Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]]. | ||
== Overview == | == Overview == | ||
Wikipedia, a collaborative Wiki-based encyclopedia, has be- come a huge phenomenon among Internet users. It covers huge number of concepts of various fields such as Arts, Geography, History, Science, Sports and Games. Since it is becoming a database storing all human knowledge, [[Wikipedia]] mining is a promising approach that bridges the Semantic Web and the Social Web (a. k. a. Web 2.0). In fact, in the previ- ous researches on Wikipedia mining, it is strongly proved that Wikipedia has a remarkable capability as a corpus for knowledge extraction, espe- cially for [[relatedness]] measurement among concepts. However, semantic relatedness is just a numerical strength of a relation but does not have an explicit relation type. To extract inferable semantic relations with ex- plicit relation types, authors need to analyze not only the link structure but also texts in Wikipedia. In this paper, authors propose a consistent approach of semantic relation extraction from Wikipedia. The method consists of three sub-processes highly optimized for Wikipedia mining; 1) fast pre- processing, 2) POS (Part Of Speech) tag tree analysis, and 3) mainstay extraction. Furthermore, detailed evaluation proved that link struc- ture mining improves both the accuracy and the scalability of semantic relations extraction. | Wikipedia, a collaborative Wiki-based encyclopedia, has be- come a huge phenomenon among Internet users. It covers huge number of concepts of various fields such as Arts, Geography, History, Science, Sports and Games. Since it is becoming a database storing all human knowledge, [[Wikipedia]] mining is a promising approach that bridges the Semantic Web and the Social Web (a. k. a. Web 2.0). In fact, in the previ- ous researches on Wikipedia mining, it is strongly proved that Wikipedia has a remarkable capability as a corpus for knowledge extraction, espe- cially for [[relatedness]] measurement among concepts. However, semantic relatedness is just a numerical strength of a relation but does not have an explicit relation type. To extract inferable semantic relations with ex- plicit relation types, authors need to analyze not only the link structure but also texts in Wikipedia. In this paper, authors propose a consistent approach of semantic relation extraction from Wikipedia. The method consists of three sub-processes highly optimized for Wikipedia mining; 1) fast pre- processing, 2) POS (Part Of Speech) tag tree analysis, and 3) mainstay extraction. Furthermore, detailed evaluation proved that link struc- ture mining improves both the accuracy and the scalability of semantic relations extraction. | ||
+ | |||
+ | == Embed == | ||
+ | === Wikipedia Quality === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Nakayama, Kotaro; Hara, Takahiro; Nishio, Shojiro. (2008). "[[Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology]]". | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === English Wikipedia === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | {{cite journal |last1=Nakayama |first1=Kotaro |last2=Hara |first2=Takahiro |last3=Nishio |first3=Shojiro |title=Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology |date=2008 |url=https://wikipediaquality.com/wiki/Wikipedia_Link_Structure_and_Text_Mining_for_Semantic_Relation_Extraction_Towards_a_Huge_Scale_Global_Web_Ontology}} | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === HTML === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Nakayama, Kotaro; Hara, Takahiro; Nishio, Shojiro. (2008). &quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_Link_Structure_and_Text_Mining_for_Semantic_Relation_Extraction_Towards_a_Huge_Scale_Global_Web_Ontology">Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology</a>&quot;. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | |||
+ | |||
+ | [[Category:Scientific works]] |
Latest revision as of 00:51, 22 March 2021
Authors | Kotaro Nakayama Takahiro Hara Shojiro Nishio |
---|---|
Publication date | 2008 |
Links | Original |
Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology - scientific work related to Wikipedia quality published in 2008, written by Kotaro Nakayama, Takahiro Hara and Shojiro Nishio.
Overview
Wikipedia, a collaborative Wiki-based encyclopedia, has be- come a huge phenomenon among Internet users. It covers huge number of concepts of various fields such as Arts, Geography, History, Science, Sports and Games. Since it is becoming a database storing all human knowledge, Wikipedia mining is a promising approach that bridges the Semantic Web and the Social Web (a. k. a. Web 2.0). In fact, in the previ- ous researches on Wikipedia mining, it is strongly proved that Wikipedia has a remarkable capability as a corpus for knowledge extraction, espe- cially for relatedness measurement among concepts. However, semantic relatedness is just a numerical strength of a relation but does not have an explicit relation type. To extract inferable semantic relations with ex- plicit relation types, authors need to analyze not only the link structure but also texts in Wikipedia. In this paper, authors propose a consistent approach of semantic relation extraction from Wikipedia. The method consists of three sub-processes highly optimized for Wikipedia mining; 1) fast pre- processing, 2) POS (Part Of Speech) tag tree analysis, and 3) mainstay extraction. Furthermore, detailed evaluation proved that link struc- ture mining improves both the accuracy and the scalability of semantic relations extraction.
Embed
Wikipedia Quality
Nakayama, Kotaro; Hara, Takahiro; Nishio, Shojiro. (2008). "[[Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology]]".
English Wikipedia
{{cite journal |last1=Nakayama |first1=Kotaro |last2=Hara |first2=Takahiro |last3=Nishio |first3=Shojiro |title=Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology |date=2008 |url=https://wikipediaquality.com/wiki/Wikipedia_Link_Structure_and_Text_Mining_for_Semantic_Relation_Extraction_Towards_a_Huge_Scale_Global_Web_Ontology}}
HTML
Nakayama, Kotaro; Hara, Takahiro; Nishio, Shojiro. (2008). "<a href="https://wikipediaquality.com/wiki/Wikipedia_Link_Structure_and_Text_Mining_for_Semantic_Relation_Extraction_Towards_a_Huge_Scale_Global_Web_Ontology">Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology</a>".