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Knowledge graph relational data extraction method based on semantic syntax interaction network

A technology of knowledge graph and relational data, which is applied in the field of complex equipment design process design knowledge graph entity relationship extraction

Active Publication Date: 2020-06-05
ZHEJIANG UNIV +1
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Problems solved by technology

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

Method used

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  • Knowledge graph relational data extraction method based on semantic syntax interaction network
  • Knowledge graph relational data extraction method based on semantic syntax interaction network
  • Knowledge graph relational data extraction method based on semantic syntax interaction network

Examples

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Embodiment

[0054] This example uses the aero engine design manual data set as a specific example for description. The data set is based on text data such as aero engine design manuals, gas turbine performance analysis and known aero engine design ontology to obtain the design documents contained Entities, and then implement the knowledge graph relational data extraction method based on semantic syntactic interaction network. Among them, the implementation of the knowledge graph relational data extraction method based on the semantic syntax interactive network includes such figure 1 The steps shown:

[0055] S1. Collect design documents for the aero engine design process. Use remote supervision methods to extract sentences containing more than two entities from text data such as aero engine design manuals and gas turbine performance analysis, and mark the relationships between entities.

[0056] S2. Perform text preprocessing for the collected design documents;

[0057] S21. Use the natural ...

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Abstract

The invention discloses a knowledge graph relational data extraction method based on a semantic syntax interaction 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; performing text preprocessing on the design document text data; establishing a relation extraction model based on a semantic syntax multi-round interaction deep neural network; inputting the preprocessed text data and the relationship type label into a relationship extraction model for offline training; and preprocessing the text data of the entity relationship to be predicted, and inputting the preprocessed text data into the trained relationship extraction model to obtain a predicted relationship category. Through multiple rounds of interaction of the semantic information and the syntactic information, the utilization rate of the semantic information and the syntactic information is improved, the semantic information and the syntactic information beneficial to knowledge graph relation data extraction are dynamically and deeply mined, and the flexibility, generalization and accuracy of the model are improved.

Description

Technical field [0001] The invention relates to a method for processing knowledge graph data in the field of computer big data, in particular to a method for extracting the entity relationship of the knowledge graph from a complex equipment design process based on a semantic syntax interactive network. Background technique [0002] The complex equipment design process will produce a large amount of unstructured text knowledge such as requirements analysis documents, design instructions, design manuals, performance analysis documents and so on. The effective mining of these textual knowledge has an important guiding role in the subsequent design process. One of the key technologies involved in mining the knowledge in the design documents of the design process is to mine the relationship categories between the knowledge entities expressed in the design documents. [0003] Data-driven entity relationship extraction methods for design documents of complex equipment design process are ...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F40/211G06F40/242G06F40/295G06F40/30
CPCG06F16/367
Inventor 刘振宇张栋豪郏维强谭建荣
Owner ZHEJIANG UNIV
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