Feature tensor-based Chinese knowledge graph representation learning method

Abstract

The invention provides a feature tensor-based Chinese knowledge graph representation learning method. The method comprises the following steps: carrying out data preparation, establishing a data structure, constructing an entity feature vector matrix, definiing relation vectors and distance formulas of marked triples, obtaining a training set, training a knowledge graph representation learning model, updating model parameters and iterative training, and carrying out relationship prediction on the unmarked triad by using the model, and carrying out iterative training again until a new unmarkedtriad cannot be learned. According to the method, feature tensors are formed by using Chinese pinyin, character information, word information and description information and are converted into featurevectors, so that a method for randomly initializing entity vectors in traditional knowledge representation learning is replaced, and Chinese features are fully utilized. Besides, a double-layer iteration mode is adopted to supplement the training corpus, so that the relation matrix can be continuously corrected, and the precision and convergence speed of the knowledge graph representation learning model are improved.

Description

technical field [0001] The invention relates to the field of knowledge graphs, in particular to a feature tensor-based Chinese knowledge graph representation learning method. Background technique [0002] The knowledge graph describes the complex relationship between concepts and entities in the objective world in a structured form, and provides a better ability to organize, manage and understand the massive information on the Internet. Knowledge graph technology usually includes three aspects of research content: knowledge representation, knowledge graph construction and knowledge graph application. Granularity expresses the semantics presented by the objective world. First of all, it is necessary to understand how humans represent knowledge and use them to solve problems, and then formalize it into an expression form that computers can reason and calculate, establish a knowledge-based system, and provide intelligent knowledge services. At the same time, knowledge represe...

AI Technical Summary

Problems solved by technology

[0006] In summary, the knowledge representation based on symbolic logic and the open knowledge representation method of Internet resources enable knowledge to have a clear semantic definition, but there is a problem of data sparsity, which makes it difficult to implement large-scale knowledge graph applications; knowledge representation based on deep learning can combine knowledge units (entities, relations, and rules) are mapped to low-dimensional continuous real space representations, but lack explicit semantic definitions

[0007] In addition, there are many foreign studies on knowledge graph representation learning, but only limited to English knowledge graphs. Due to language differences, English words only have simple string information and phrase information. When learning knowledge representation, only random initialization vectors are needed. Yes, but Chinese contains a wealth of semantic information. Existing research methods cannot achieve good results on Chinese knowledge graphs. At present, China is still at the stage of how to construct knowledge graphs, and there is a lack of research on the representation learning of Chinese knowledge graphs.

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