SemOIR: An ontology-based semantic information retrieval system

被引：3
|
作者：
Tang, Mingwei [1 ]
Chen, Jiangping [2 ]
Chen, Haihua [2 ]
机构：
[1] Nanjing Audit Univ, Sch Informat Engn, Nanjing 211815, Jiangsu, Peoples R China
[2] Univ North Texas, Dept Informat Sci, Denton, TX 76207 USA
来源：
COMPANION OF THE 2020 IEEE 20TH INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY, AND SECURITY (QRS-C 2020) | 2020年
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1109/QRS-C51114.2020.00042
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
This paper describes a semantic vector space model (SeVSM) and an information retrieval system based on the model. The SeVSM aims to improve information retrieval performance for domain-specific systems. In this model, we use an ontology to build the relations between any two keywords to solve the performance deficiency caused by the basic hypothesis of a vector space model (VSM) where keywords are mutually independent. Then we designed and developed a semantic ontology-based information retrieval (SemOIR) system based on the SeVSM model. An experimental study using 15 queries from different domains confirms the effectiveness of the SeVSM and the usability of the SemOIR system. The proposed model and the system contribute significantly to the application of semantic retrieval for digital libraries and e-commerce systems.
引用
收藏
页码：204 / 208
页数：5
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