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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| + | | 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. | ||
Revision as of 00:42, 18 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.
