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Data association method based on first-order logic and nerve network

A neural network algorithm and neural network technology, applied in the field of data correspondence based on first-order logic and neural network, can solve problems such as imperfection, unutilized matching information, waste of time, etc., to improve efficiency and accuracy, and reduce time. Effect

Inactive Publication Date: 2012-11-28
三亚哈尔滨工程大学南海创新发展基地
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Problems solved by technology

[0004] Although the method described above can solve some matching problems in pattern matching, it is not perfect, and the historical matching information has not been used. As a result, some known rules still need to be checked next time when performing data-corresponding operations. The matching algorithm is used for re-matching, which not only wastes time but also affects the accuracy of matching

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  • Data association method based on first-order logic and nerve network
  • Data association method based on first-order logic and nerve network
  • Data association method based on first-order logic and nerve network

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Embodiment Construction

[0032] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0033] (1) Main processing process

[0034] figure 1It is the flowchart of the TIAFL (Table Information Acquisition Based on First-order Logic) algorithm for table feature extraction based on first-order logic. Indicates that it is stored in the collection, including the table name, positive sample data, negative sample data, and assertion collection; secondly, use the table feature extraction algorithm of first-order logic to extract features from each table in the collection; finally, extract each The characteristics of the table are stored for later use in table identification.

[0035] figure 2 Using the extracted features to perform table matching flow chart, the steps can be summarized as follows: first, extract the table information in the pattern to be matched, and store the result in the table matching set; secondly, traverse the table matching set in...

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Abstract

The invention belongs to the field of data migration and data integration, and in particular relates to a data association method based on a first-order logic and a nerve network. The data association method is high in matching efficiency and high in accuracy. The method comprises the following steps of: (1) analyzing a matched data mode; (2) converting the mode into a table vector, and storing into a table training set to be matched; (3) performing characteristic extraction on tables in the set; (4) storing the extracted characteristics of the tables; (5) matching a table to be matched in a mode to be matched; (6) training fields in the matched mode, and correcting an expression of the fields and the constructed nerve network; and (7) performing field matching on the matched table by using the trained nerve network and the corrected field expression format. By the method, the time during data association is shortened, and the matching efficiency and the accuracy are improved.

Description

technical field [0001] The invention belongs to the fields of data migration and data integration, and in particular relates to a data correspondence method based on first-order logic and neural network with high matching efficiency and accuracy. Background technique [0002] With the continuous development of network and database technology, the types and quantities of data are also increasing. Therefore, technical issues such as sharing and mutual conversion of heterogeneous data have become urgent problems to be solved. In the fields of semantic WEB, data warehouse, P2P database, schema integration and e-commerce, etc., the sharing and mutual conversion of heterogeneous data have been deeply studied. As the first step to realize heterogeneous data sharing, pattern matching plays an irreplaceable role in the whole data processing process. At present, the conversion of heterogeneous data is mostly done manually by operators, which requires operators to be familiar with dat...

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Application Information

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IPC IPC(8): G06F17/30G06N3/08
Inventor 黄少滨刘国峰朴秀峰申林山刘刚刘建华
Owner 三亚哈尔滨工程大学南海创新发展基地
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