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

Abstract

The invention discloses a header column entity relationship matching method based on a deep learning multi-head selection model, which comprises the following steps of: defining data entity attribute categories such as time, names, company names and the like for data items of a table, and constructing a regular recognition method; constructing artificial features of any two column combinations of the header; enabling the header character sequence and the data attribute sequence corresponding to the header to pass through respective embedding layers; enabling the coding layer to adopt a bi-lstm model structure; carrying out pairwise combination splicing on the context coding information at any position based on a multi-head selection mechanism; calculating a binary loss value of any two positions of the header sequence for each relation category; and reserving and serving the model with the loss value converged to the best as a model used for prediction. The invention discloses a header column entity relationship matching method based on a deep learning multi-head selection model. The method has certain convenience through a header and a 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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