Formal semantic reasoning and deep learning-based natural language knowledge mining method

Abstract

The invention discloses a formal semantic reasoning and deep learning-based natural language knowledge mining method. The method comprises the following steps: a machine learning step used for combining a formal semantic reasoning method with a machine learning method and performing machine learning including semanteme, an intention learning step used for learning a descriptive intention of a to-be-mined natural language, a text topic extraction step used for analyzing a text topic content and paragraph content descriptive intention according to an LDA model, and a document structure classification model building step used for performing model training according to a deep convolutional neural network, automatically modifying and perfecting the neural network in a training process and building an automatic document structure classifier. According to the method, a non-structured data group represented by literatures, voices and the like is directly processed and the intention is reasoned by depending on the combination of automated semantic reasoning, a deep learning technology and natural language processing, thereby obtaining a real intention of text description; and mined and retrieved knowledge is used for analysis and construction of a new knowledge structure, so that knowledge mining and retrieval can be performed in an efficient, intelligent, learnable and self-evolution manner.

Description

technical field [0001] The invention relates to the field of knowledge mining, in particular to a method for knowledge mining of natural language by using formal semantic reasoning and deep learning technology. Background technique [0002] With the development of artificial intelligence, there is an increasing need for knowledge and semantic mining of natural language in daily life. In the prior art, when natural language is mined, word decomposition is usually used. For example, when natural language is obtained, the sentence is divided into multiple words, and these words are used as keywords to construct knowledge, thereby obtaining the natural language. The main information of the language. [0003] However, the above language mining methods cannot reflect the characteristics and connotation of natural language well. Human language has diverse expressions and complex structural characteristics. The same meaning can be expressed in many ways, and the same expression ca...

AI Technical Summary

Problems solved by technology

Therefore, when mining a piece of natural language, due to the poor data structure of the overall text, multi-source and heterogeneous data, etc., the results of language mining can only express semantics mechanically, but cannot adapt to natural language in a real language environment. knowledge connotation

[0004] In addition, when the existing technology processes unstructured data groups, it usually uses common databases for search and analysis, which cannot quickly and accurately locate, and the processing process is not efficient enough, nor can it realize intelligent knowledge mining

Images