Least Common Subsumer Trees for Plan Retrieval

Author(s):  
Antonio A. Sánchez-Ruiz ◽  
Santiago Ontañón
Keyword(s):  
Author(s):  
D. Sun ◽  
S. Zhao ◽  
Z. Zhang ◽  
X. Shi

The structure of the emergency plan on earthquake is complex, and it’s difficult for decision maker to make a decision in a short time. To solve the problem, this paper presents a match method based on Latent Semantic Analysis (LSA). After the word segmentation preprocessing of emergency plan, we carry out keywords extraction according to the part-of-speech and the frequency of words. Then through LSA, we map the documents and query information to the semantic space, and calculate the correlation of documents and queries by the relation between vectors. The experiments results indicate that the LSA can improve the accuracy of emergency plan retrieval efficiently.


Author(s):  
Andrea Bonisoli ◽  
Alfonso Emilio Gerevini ◽  
Alessandro Saetti ◽  
Ivan Serina
Keyword(s):  

2016 ◽  
Vol 32 (10) ◽  
pp. 1339-1343 ◽  
Author(s):  
Zhengdong Zhou ◽  
Wenwen Zhang ◽  
Shaolin Guan

2016 ◽  
Vol 149 (1-2) ◽  
pp. 209-240 ◽  
Author(s):  
Mauro Vallati ◽  
Ivan Serina ◽  
Alessandro Saetti ◽  
Alfonso Emilio Gerevini
Keyword(s):  

Author(s):  
Markus Weber ◽  
Christoph Langenhan ◽  
Thomas Roth-Berghofer ◽  
Marcus Liwicki ◽  
Andreas Dengel ◽  
...  

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