Clustering of Rough Set Related Documents with Use of Knowledge from DBpedia

被引：0

作者：

Szczuka, Marcin ^{[1
]}

Janusz, Andrzej ^{[1
]}

Herba, Kamil ^{[1
]}

机构：

[1] Univ Warsaw, Fac Math Informat & Mech, PL-02097 Warsaw, Poland

来源：

ROUGH SETS AND KNOWLEDGE TECHNOLOGY | 2011年 / 6954卷

关键词：

Text mining; semantic clustering; DBpedia; document grouping; rough sets;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A case study of semantic clustering of scientific articles related to Rough Sets is presented. The proposed method groups the documents on the basis of their content and with assistance of DBpedia knowledge base. The text corpus is first treated with Natural Language Processing tools in order to produce vector representations of the content and then matched against a collection of concepts retrieved from DBpedia. As a result, a new representation is constructed that better reflects the semantics of the texts. With this new representation, the documents are hierarchically clustered in order to form partition of papers that share semantic relatedness. The steps in textual data preparation, utilization of DBpedia and clustering are explained and illustrated with results of experiments performed on a corpus of scientific documents about rough sets.

引用

页码：394 / 403

页数：10

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