Title :
A Heterogeneous Data Matching Algorithm with Combining First-Order Logic and Semantic Similarity
Author :
Gang Liu ; Caixia Lu ; Shaobin Huang ; Suyan Sun
Author_Institution :
Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin, China
Abstract :
In this paper studies a kind of information matching method based on neural network, and the method combines first-order logic and semantic similarity to complete the table matching, using SOM+ to generate feedback neural network to complete the field matching. The method can effectively reduce the matching time complexity. And by using history match, it effectively reduces the training time of neural network, improving the accuracy of matching.
Keywords :
computational complexity; data handling; formal logic; learning (artificial intelligence); neural nets; pattern matching; SOM+; feedback neural network; field matching; first-order logic; heterogeneous data matching algorithm; history match; information matching method; matching time complexity; neural network; neural network training time; semantic similarity; table matching; Biological neural networks; Cities and towns; Educational institutions; Pattern matching; Semantics; Training;
Conference_Titel :
Information Science and Applications (ICISA), 2014 International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4799-4443-9
DOI :
10.1109/ICISA.2014.6847329
