Fast semantic object search and detection for vegetable trading information using Steiner tree

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• 作者：Ming Zhao (1)
  Tengyang Tao (1)
  Xiaoyin Duanmu (2)
  
• 关键词：Object ; Detection ; Search ; Steiner tree ; Information extraction
• 刊名：Artificial Intelligence Review
• 出版年：2014
• 出版时间：March 2014
• 年：2014
• 卷：41
• 期：3
• 页码：415-427
• 全文大小：999 KB
• 作者单位：Ming Zhao (1)
  Tengyang Tao (1)
  Xiaoyin Duanmu (2)
  
  1. College of Information and Electrical Engineer, China Agricultural University, 100083, Beijing, China
  2. Technique and Quality Department, Nanjing Electrical Equipment Ltd., 210002, Nanjing, China
  
• ISSN：1573-7462

文摘

We propose an approach to speed up the semantic object search and detection for vegetable trading information using Steiner Tree. Through analysis, comparing the relevant ontology construction method, we present a set of ontology construction methods based on domain ontology for vegetables transaction information. With Jena2 provides rule-based reasoning engine, More related information could be searched with the help of ontology database and ontology reasoning, query expansion is to achieve sub-vocabulary of user input, the parent class of words, equivalence class of extensions, and use of ontology reasoning to get some hidden information to use of these technologies, we design and implementation of ontology-based semantic vegetables transaction information retrieval system, and through compare to keyword-based matching of large-scale vegetable trading site retrieval systems, the results show that the recall and precision rate of ontology-based information retrieval system much better than keyword-based information retrieval system, and has some practical value.