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	Learning ontology resolution for document representation and its applications in text mining
	Bing, Lidong ; Sun, Bai ; Jiang, Shan ; Zhang, Yan ; Lam, Wai
	2010
英文摘要	It is well known that synonymous and polysemous terms often bring in some noises when calculating the similarity between documents. Existing ontology-based document representation methods are static, hence, the chosen semantic concept set for representing a document has a fixed resolution and it is not adaptable to the characteristics of a document collection and the text mining problem in hand. We propose an Adaptive Concept Resolution (ACR) model to overcome this issue. ACR can learn a concept border from an ontology taking into consideration of the characteristics of a particular document collection. Then this border can provide a tailor-made semantic concept representation for a document coming from the same domain. Another advantage of ACR is that it is applicable in both classification task where the groups are given in the training document set, and clustering task where no group information is available. Furthermore, the result of this model is not sensitive to the model parameter. The experimental results show that ACR outperforms an existing static method significantly. ? 2010 ACM.; EI; 0
语种	英语
DOI标识	10.1145/1871437.1871711
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/329644]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Bing, Lidong,Sun, Bai,Jiang, Shan,et al. Learning ontology resolution for document representation and its applications in text mining. 2010-01-01.

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