Natural language knowledge exploration system based on formal semantics reasoning and deep learning

Abstract

The invention discloses a natural language knowledge exploration system based on formal semantics reasoning and deep learning. The system comprises a machine learning module combining formal semantic reasoning and a machine learning method, and conducting machine learning containing semantics, an intention learning module learning a description intention of a to-be-explored natural language, a text subject extraction module analyzing a text subject content and paragraph content description intention via an LDA model, and a file structure classification model building module conducting model training based on a deep convolution neural network, automatically decorating and improving the neural network during the training, and building an automatic file structure classifier. Based on combination of automatic semantic reasoning, deep learning technology and natural language processing, non-structural data groups with documents and voices as representatives can be processed and reasoned in light of intentions, so real intention of verbal description can be learned; knowledge explored and retrieved can be used for new knowledge structure analysis and construction, so knowledge can be explored and retrieved in a way with high-efficiency, intelligence and learnability and self-evolution.

Description

technical field [0001] The invention relates to the field of knowledge mining, in particular to a system 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

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