Medical achievement recommendation method and system based on entity relation mapping

An entity relationship, recommendation method technology, applied in text database query, unstructured text data retrieval, semantic tool creation, etc., can solve the impact, positive example triple score is not small enough, inversion relationship mode is no longer effective, etc. problems, to achieve the effect of ensuring accuracy, improving accuracy, and eliminating defects

CN114880429APending Publication Date: 2022-08-09QILU UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2022-08-09

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Abstract

The invention provides a medical achievement recommendation method based on entity relation mapping, and belongs to the technical field of research achievement recommendation, and the scheme comprises the following steps: obtaining a pre-constructed medical knowledge graph; based on a pre-trained knowledge representation model, performing vectorization representation on entities in the medical knowledge graph; wherein the knowledge representation model is constructed based on an entity relation mapping matrix and multi-mode depth embedding, the entity relation mapping matrix is generated through projection vectors, and a relation mapping matrix in a TransR model is replaced; meanwhile, a multi-mode deep embedding concept is introduced, and a reverse translation geometric distance embedding model and a symmetric relation embedding model are added into entity and relation modeling; and based on the obtained entity vectorization representation, determining the similarity between the entities so as to obtain the research result recommendation of the disease concerned by the user.
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Description

technical field

[0001] The present disclosure belongs to the technical field of research achievement recommendation, and in particular relates to a method and system for recommending medical achievements based on entity relationship mapping. Background technique

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] In the medical field, knowledge graph has a very wide range of applications, and medical knowledge graph is of great significance in clinical decision support, medical intelligent semantic retrieval, medical question answering and other fields. Social media has the potential to transform public health, including disseminating health updates, sharing disease information, and more. The use of social media to disseminate the latest research results of related diseases, so that patients can obtain the latest research progress, will be very beneficial for pa...

Examples

Embodiment 1

[0049] The purpose of this embodiment is to provide a method for recommending medical results based on entity relationship mapping.

[0050] like figure 1 As shown, a medical outcome recommendation method based on entity relationship mapping, including:

[0051] Obtaining a pre-built medical knowledge graph; wherein, the medical knowledge graph includes entities composed of user information, disease information, and disease-related research result information obtained from the medical social platform, and the relationship between the entities;

[0052] Based on the pre-trained knowledge representation model, the entities in the medical knowledge graph are represented by vectorization; wherein, the knowledge representation model is constructed based on the entity relationship mapping matrix and the multi-modal deep embedding, and the entity relationship mapping matrix is ​​generated by the projection vector, Replace the relationship mapping matrix in the TransR model; at the s...

Embodiment 2

[0120] The purpose of this embodiment is to provide a medical achievement recommendation system based on entity relationship mapping.

[0121] A medical outcome recommendation system based on entity relationship mapping, including:

[0122] A data acquisition unit, which is used to acquire a pre-built medical knowledge graph; wherein, the medical knowledge graph includes entities composed of user information, disease information, and disease-related research result information obtained from the medical social platform, as well as between entities. Relationship;

[0123] an entity vectorization representation unit, which is used for vectorized representation of entities in the medical knowledge graph based on a pre-trained knowledge representation model; wherein, the knowledge representation model is constructed based on an entity relationship mapping matrix and a multi-modal deep embedding, The entity relationship mapping matrix is ​​generated by the projection vector, which ...