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Rail transit standard entity relationship automatic completion method based on artificial intelligence

A rail transit and entity relationship technology, applied in the creation of semantic tools, natural language data processing, unstructured text data retrieval, etc., can solve the problems of low accuracy, time-consuming and labor-intensive, etc. The effect of improving structural accuracy

Active Publication Date: 2020-08-28
XIAN UNIV OF TECH
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Problems solved by technology

[0007] The purpose of the present invention is to provide an artificial intelligence-based rail transit specification entity relationship automatic completion method, which solves the problem that the existing rail transit specification entity relationship completion method can only be done manually, which is time-consuming and laborious, and has low accuracy

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  • Rail transit standard entity relationship automatic completion method based on artificial intelligence
  • Rail transit standard entity relationship automatic completion method based on artificial intelligence
  • Rail transit standard entity relationship automatic completion method based on artificial intelligence

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Embodiment Construction

[0045] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0046] A kind of artificial intelligence-based rail transit standard entity relationship automatic completion method of the present invention, refer to figure 1 , including the following steps:

[0047] Step 1: Construct an entity-relationship completion model according to rail transit specifications

[0048] Step 1.1: Obtain the original data of the rail transit specification from the urban rail transit technical specification, check the format of the obtained original data, delete unnecessary information, such as spaces, etc., obtain the preprocessed data, and then analyze the preprocessed data Perform training and generate a dictionary;

[0049] Step 1.2: Process the data in the dictionary, mine missing features, and extract entity completion rules and methods;

[0050] Step 1.3: Construct an entity-relationship completion model using t...

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Abstract

The invention discloses a rail transit standard entity relationship automatic completion method based on artificial intelligence. The method comprises the steps of constructing an entity relationshipcompletion model, inputting the rail transit specification and part-of-speech segmentation of nouns into an entity relationship completion model; judging whether the input specification is a simple sentence or not; if yes, searching entity related attributes in the rail transit specification; generating an entity relationship triple; if not, extracting later sentence attribute words and entities of the rail transit specification, matching the former sentence entities and later sentence attribute words in a n:n manner, or judging whether the the former sentence grammar is subject-verb-object ornot and the latter sentence grammar is object complement; if yes, directly matching the former sentence entities with the objects and directly matching the later sentence keywords with the object entities to generate entity relationship triples, and if not, outputting the entities of which the vocabulary relevancy exceeds a threshold value and the entity relationships to generate the entity relationship triples to obtain complete semantic structure entity specifications, thereby finishing automatic completion of the rail transit specification entity relationships.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence natural language processing, and relates to an artificial intelligence-based automatic completion method for rail transit specification entity relations. Background technique [0002] The knowledge graph is a semantic knowledge base, and the knowledge graph uses triples to store knowledge. Knowledge graphs can promote computers to better understand natural language, provide better services for people, and realize natural switching between humans and computers. Entity recognition and relationship extraction are important processes for building knowledge graphs. [0003] Due to the complex Chinese grammar, the sentence structure is out of order, and there are no strict restrictions. Some standard documents lack entity components, but the description text can be generated if the basic semantics are met. These reasons have led to great differences in the writing of norms in various pr...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F40/242G06F40/279G06F16/33
CPCG06F16/367G06F40/279G06F40/242G06F16/3331
Inventor 朱磊冯林林黑新宏刘尧林吕泓瑾张晋源林泓刘瑞刘旭华
Owner XIAN UNIV OF TECH
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