A Heterogeneous Data Matching Algorithm with Combining First-Order Logic and Semantic Similarity

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...

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Bibliographic Details
Published in2014 International Conference on Information Science & Applications (ICISA) pp. 1 - 4
Main Authors Gang Liu, Caixia Lu, Shaobin Huang, Suyan Sun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2014
Subjects
Biological neural networks
Cities and towns
Educational institutions
Pattern matching
Semantics
Training
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ISBN9781479944439
1479944432
ISSN2162-9048
DOI10.1109/ICISA.2014.6847329

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Summary: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.
ISBN:9781479944439
1479944432
ISSN:2162-9048
DOI:10.1109/ICISA.2014.6847329