Using semantic distance in a content-based heterogeneous information retrieval system

被引：0
|
作者：
El Sayed, Ahmad [1 ]
Hacid, Hakim [1 ]
Zighed, Djamel [1 ]
机构：
[1] Univ Lyon 2, ERIC Lab, F-69676 Bron, France
来源：
MINING COMPLEX DATA | 2008年 / 4944卷
关键词：
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：
This paper brings two contributions in relation with the semantic heterogeneous (documents composed of texts and images) information retrieval: (1) A new context-based semantic distance measure for textual data, and (2) an IR system providing a conceptual and an automatic indexing of documents by considering their heterogeneous content using a domain specific ontology. The proposed semantic distance measure is used in order to automatically fuzzify our domain ontology. The two proposals are evaluated and very interesting results were obtained. Using our semantic distance measure, we obtained a correlation ratio of 0.89 with human judgments on a set of words pairs which led our measure to outperform all the other measures. Preliminary combination results obtained on a specialized corpus of web pages are also reported.
引用
收藏
页码：224 / 237
页数：14
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