Entity relationship prediction method and prediction system based on knowledge representation learning

A knowledge representation and entity relationship technology, applied in knowledge expression, prediction, unstructured text data retrieval, etc., can solve problems such as incomplete knowledge graphs and inability to mine relationships

Inactive Publication Date: 2019-01-15
THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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AI Technical Summary

Problems solved by technology

[0006] The matching technology used in the existing knowledge map-based retrieval technology can only search for existing relationships, but cannot dig out the relationships hidden in the data. Sometimes due to the limitations of data collection, the latter data loss, etc., will cause knowledge map It is incomplete, but using the technology of knowledge represen

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  • Entity relationship prediction method and prediction system based on knowledge representation learning
  • Entity relationship prediction method and prediction system based on knowledge representation learning
  • Entity relationship prediction method and prediction system based on knowledge representation learning

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Embodiment Construction

[0062] The present invention provides an entity relationship prediction algorithm based on knowledge representation learning, the specific steps are as follows:

[0063] S1: The knowledge preparation module transforms the existing data and information into the knowledge form described by RDF triples to form a triple set S, S={(h,r,t)}, where (h,r,t) Represents a triple, h represents the head entity, r represents the relationship, and t represents the tail entity, specifically:

[0064] S1-1: Extract triplets from unstructured information:

[0065] Extract the subject, predicate, and object from a sentence of text to form a piece of knowledge, which is recorded as an RDF triple and abstracted into a formula as (h, r, t). For example, extract the subject, predicate, and object from "The President of the United States is Obama", and record it as a triplet in the form of (United States, President, Obama), where the head entity h represents the United States, the relation r repres...

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Abstract

The invention discloses an entity relationship prediction method and a prediction system based on knowledge representation learning. The method comprises four modules, namely, knowledge preparation, knowledge representation model construction, knowledge representation model training and entity relationship prediction. The knowledge preparation module completes the data preparation and builds the knowledge map. The knowledge representation model construction module completes the construction of the model, which eliminates the semantic differences among different types of entities through projection operation; the training module of knowledge representation model forms the final knowledge representation model based on the parameters of the iterative training knowledge representation model ofknowledge map. Entity relationship prediction module can predict the possible relationship between any given two entities. The method of the invention predicts entity relationship based on knowledgemap, projects different types of entities to the same semantic space through a spatial projection algorithm, and performs calculation operation, thereby achieving high reliability of prediction results.

Description

technical field [0001] The invention belongs to the field of relationship prediction between entities, and relates to an entity relationship prediction method and a prediction system based on knowledge graphs and machine learning. Background technique [0002] In the future network environment, battlefield information and various intelligence information are widely distributed, with fragmented and fragmented distribution characteristics, and intelligence information is often hidden in scattered fragmented information. In the fast-paced war environment in the future, the information service system must be able to discover the hidden relationship between information from scattered information, quickly extract valuable information, and quickly provide commanders with high-quality information support. [0003] At present, most scholars' research directions on entity relationship mainly focus on entity relationship extraction and entity relationship reasoning. The research on en...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06N5/02G06Q10/04
CPCG06N5/02G06Q10/04
Inventor 李友江吴姗姗荀智德丁蔚然葛唯益姜晓夏王羽
Owner THE 28TH RES INST OF CHINA ELECTRONICS TECH GROUP CORP
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