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

fYear

2014

fDate

6-9 May 2014

Firstpage

1

Lastpage

4

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science and Applications (ICISA), 2014 International Conference on

Conference_Location

Seoul

Print_ISBN

978-1-4799-4443-9

Type

conf

DOI

10.1109/ICISA.2014.6847329

Filename

6847329