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Knowledge graph relational data classification method based on syntactic attention neural network

A technology of knowledge graph and neural network, which is applied in the field of entity relationship classification of knowledge graph in complex equipment design process, to achieve the effect of improving the accuracy of prediction and improving flexibility

Active Publication Date: 2020-05-19
ZHEJIANG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

And this method can be widely used in the design process of various complex equipment in the process of classifying the entity relationship of design documents

Method used

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  • Knowledge graph relational data classification method based on syntactic attention neural network
  • Knowledge graph relational data classification method based on syntactic attention neural network
  • Knowledge graph relational data classification method based on syntactic attention neural network

Examples

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Embodiment

[0050] This example uses the aero-engine design manual data set as a specific example for illustration. The data set is based on text data such as aero-engine design manuals, gas turbine performance analysis, and known design knowledge ontology to obtain the entities contained in the design documents. Then, the implementation of knowledge map relational data classification method based on syntactic attention neural network. Among them, the implementation of the knowledge map relational data classification method based on the syntactic attention neural network includes as follows: figure 1 Steps shown:

[0051] S1. Collect design documents of the aeroengine design process. The remote supervision method is used to extract sentences containing more than two entities in text data such as aero-engine design manuals and gas turbine performance analysis, and the relationship between entities is marked.

[0052] S2. Perform text preprocessing on the collected design document...

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Abstract

The invention discloses a knowledge graph relational data classification method based on a syntactic attention neural network. The method mainly comprises the following steps: collecting a design document of a complex equipment design process, and establishing a design document corpus according to text data of the design document; carrying out text preprocessing on the collected text data of the design document; establishing an entity relationship classification model based on a syntactic attention deep neural network; inputting the preprocessing result and the category label into the model for offline training; and inputting to-be-predicted text data into the trained syntactic attention-based deep neural network, and predicting to obtain a relationship category result corresponding to thekeyword text in the text data. According to the method, semantic information and syntax information are adaptively combined, so that the accuracy of entity relationship category prediction of the text data of the design document is effectively improved, and which part of path of the dependency syntax tree of the statement has higher weight in the prediction process of the model can be deduced.

Description

technical field [0001] The present invention relates to a knowledge map data processing method in the field of computer big data, in particular to an entity relationship classification method based on a syntactic attention neural network for complex equipment design process knowledge maps. Background technique [0002] In the industrial field, a large amount of unstructured text knowledge such as requirements analysis documents, design specifications, design manuals, and performance analysis documents will be generated during the complex equipment design process. Effective mining of these textual knowledge plays an important guiding role in the design process of complex equipment. Among them, mining the knowledge in the design documents in the design process is the key to mining the relationship categories between the entities expressed in the design documents. [0003] Data-driven entity-relationship classification methods for complex equipment design process design docume...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06F16/36G06F40/211G06F40/30G06N3/04G06N3/08
CPCG06F16/35G06F16/367G06N3/08G06N3/045
Inventor 刘振宇张栋豪郏维强谭建荣
Owner ZHEJIANG UNIV
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