Header-column entity-relationship matching method based on deep learning multi-head selection model

Abstract

The invention discloses a table header column entity relationship matching method based on a deep learning multi-head selection model, including: defining data entity attribute categories for the data items of the table, such as time, name, company name, etc., and constructing a regular recognition method; constructing a table header The artificial features of any two-column combination; after the header character sequence and the data attribute sequence corresponding to the header pass through their respective embedding layers; the encoding layer adopts the bi-lstm model structure; the context encoding information is based on the multi-head selection mechanism. Splicing two combinations; calculating the binary loss value of any two positions in the header sequence for each relationship category; converging the loss value to the best model and retaining it as the model for prediction. The header column entity relationship matching method based on the deep learning multi-head selection model makes this method more convenient through the header and model.

Description

technical field [0001] The present invention relates to the technical field of header column entities, in particular to a header column entity relationship matching method based on a deep learning multi-head selection model. Background technique [0002] The header column entity relationship matching technology is to determine the corresponding relationship between the two columns of the table. This technology plays an important role in table information mining. The existing technical solution is to use data item collision for judgment, ignoring the semantic information of the table header. This method needs to maintain a benchmark database by itself before data collision can be performed. The startup cost of the scheme is relatively high, and the header semantics are not used for auxiliary judgment. Therefore, we improve this and propose a method for matching the entity relationship of header columns based on the deep learning multi-head selection model. Contents of the i...

AI Technical Summary

Problems solved by technology

[0002] The header column entity relationship matching technology is to determine the corresponding relationship between the two columns of the table. This technology plays an important role in table information mining. The existing technical solution is to use data item collision for judgment, ignoring the semantic information of the table header. This method needs to maintain a benchmark database by itself before data collision can be performed. The startup cost of the scheme is relatively high, and the header semantics are not used for auxiliary judgment

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