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School domain knowledge graph construction method based on entity recognition and attribute extraction model

A technology for entity recognition and domain knowledge, which is applied in the field of school domain knowledge map construction based on entity recognition and attribute extraction model, which can solve the problem of building school domain knowledge map without school domain recognition and attribute extraction.

Pending Publication Date: 2019-09-27
HUAIYIN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

However, there is currently no method to combine the neural network model for entity recognition and attribute extraction in the school domain and to construct a school domain knowledge graph.

Method used

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  • School domain knowledge graph construction method based on entity recognition and attribute extraction model
  • School domain knowledge graph construction method based on entity recognition and attribute extraction model
  • School domain knowledge graph construction method based on entity recognition and attribute extraction model

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Embodiment Construction

[0062] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0063] Such as Figure 1-Figure 6 As shown, a school domain knowledge map construction method of an entity recognition and attribute extraction model described in the present invention includes the following steps:

[0064] Step 1: Preprocess the dataset of questions and answers in the school field to obtain the entity recognition model annotation dataset EntityData;

[0065] Step 1.1: Define the preprocessed question-answer set QASet, define question, answer, and triple as the question, answer a...

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Abstract

The invention discloses a school domain knowledge graph construction method based on entity identification and an attribute extraction model. The method comprises the steps of firstly, preprocessing a school domain question and answer pair data set to obtain an entity recognition model annotation data set EntityData; training an entity identification model based on BERT-BiLSTM-CRF by utilizing the data set EntityData so as to obtain a school domain entity identification model SchioolEntityModel; preprocessing the school domain question and answer pair data set to obtain an attribute extraction model annotation data set AttributeData; training a BERT-based attribute extraction model by utilizing the data set AttributeData so as to obtain a school domain attribute extraction model SchioolAttributeModel; and finally, extracting the entities, attributes and attribute values in the question pair data set through the ScheolEntity Model and the ScheolAttributeModel respectively, so that a knowledge triple is established, and a school domain knowledge graph is constructed. According to the method, the school domain knowledge graph can be effectively constructed.

Description

technical field [0001] The invention belongs to the technical field of knowledge map construction, and in particular relates to a school field knowledge map construction method based on entity recognition and attribute extraction models. Background technique [0002] Entity recognition, also known as named entity recognition or entity extraction, is to automatically identify named entities from raw data corpus. Since entities are the most basic elements in knowledge graphs, their extraction integrity, accuracy, and recall will directly affect the quality of knowledge graph construction. Therefore, entity recognition is an important method to acquire knowledge from semi-structured or unstructured data, and it is the basis and key step for building a knowledge graph. [0003] Knowledge extraction is to extract available knowledge units from natural language text or multimedia content documents through automatic or semi-automatic technology. Knowledge units mainly include thre...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F16/332G06Q50/20
CPCG06F16/367G06F16/3329G06Q50/205
Inventor 朱全银王佳薇周泓冯万利李翔王文豪丁瑾金鹰高尚兵宗慧
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
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