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Relation extraction and knowledge graph construction method based on deep learning model

A technology of relationship extraction and deep learning, applied in the field of knowledge graph construction, can solve problems such as no technical means, and achieve the effect of improving interest, ensuring correctness, and enriching visual display

Active Publication Date: 2019-12-20
DATAGRAND TECH INC
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AI Technical Summary

Problems solved by technology

[0004] At present, although there are many theoretical studies on the construction of knowledge graphs for unstructured data, none of them have given a real technical method that can be implemented.

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  • Relation extraction and knowledge graph construction method based on deep learning model

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Embodiment Construction

[0034] In order to better understand the technical solutions of the present invention, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0035] It should be clear that the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0036] The present application will be described in further detail below through specific embodiments and in conjunction with the accompanying drawings.

[0037] A method for relation extraction and knowledge graph construction based on a deep learning model, specifically comprising the following steps:

[0038] S1, use the data labeling platform to process the corpus data into labeled data.

[0039] Specifically, the steps of using ...

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Abstract

The invention discloses a relation extraction and knowledge graph construction method based on a deep learning model. The relation extraction and knowledge graph construction method specifically comprises the following steps of processing the corpus data into the annotation data by utilizing a data annotation platform; inputting the annotation data into the deep learning model for training and testing, and generating a relationship extraction model; inputting the to-be-extracted text data into the relationship extraction model, and extracting the entity relationship pairs of the to-be-extracted text data; and constructing a knowledge graph of the to-be-extracted text by utilizing a graph database. According to the method, the relationship extraction model is constructed based on a BERT model, a bidirectional long-short-term memory network model and a conditional random field algorithm, the entity relationship pairs can be accurately extracted, and the correctness of the entity relationship pairs entering the graph database is ensured by manually checking the extracted entity relationship pairs.

Description

technical field [0001] The present invention relates to the technical field of knowledge graph construction, in particular to a method for relation extraction and knowledge graph construction based on a deep learning model. Background technique [0002] Currently, the construction methods of knowledge graphs vary according to the original data source. Raw data sources are mainly divided into three categories: structured data, semi-structured data, and unstructured data. Usually, structured data is stored in relational databases and non-relational databases. D2R technology can be used to convert structured data into RDF data to complete the construction of knowledge graphs. Semi-structured data refers to data that has a certain data structure and requires further analysis to obtain, such as encyclopedia data, web page data, etc. For this type of data, the data structure can be analyzed in a customized manner, and regular expressions or web page crawling analysis methods can...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/28G06F16/951
CPCG06F16/367G06F16/288G06F16/951Y02D10/00
Inventor 连明杰陈运文昝云飞孙伟伟徐华伟纪达麒
Owner DATAGRAND TECH INC
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