Character relationship graph construction method based on integration of ontology and multiple neural networks

Abstract

The invention relates to a character relationship graph construction method based on integration of an ontology and multiple neural networks. The method comprises the following steps: crawling data related to a character in a certain domain in the Internet; establishing a domain character ontology; extracting data from a structured data table which contains multiple types of entities and has repeated entities to construct a standardized entity table; matching the two class names of the character ontology model with the two entity table names through a semantic mapping algorithm, automaticallyobtaining all entity relationships, and storing the entity relationships in a Neo4j database in a graph structure; for the text data in the structured table, carrying out character entity recognitionand relationship extraction by using a sliding window, entity position characteristics and a bidirectional gating recurrent neural network; and updating the current graph structure of the newly addedrelationship to form a domain character relationship knowledge graph. The character relationship advanced features can be extracted from the original relational data and the text data, manual design is not needed, the recognition effect is improved, and the efficiency of constructing the character relationship graph by the complex webpage text is improved.

Description

technical field [0001] The invention belongs to the field of Internet big data processing, and in particular relates to a method for constructing a character relationship graph based on an ontology and a variety of neural network integrations. Background technique [0002] With the vigorous development of Internet technology and the explosive growth of data, people have been able to obtain a lot of relevant knowledge through search engines, and a large amount of knowledge is hidden in unstructured text and semi-structured tables in web pages. In the face of massive web text information, people need to extract the knowledge they need from the Internet, but with the continuous growth of knowledge, this way of knowledge acquisition can no longer meet people's needs. People expect to organize resources on the Internet in a more intelligent way, so that they can obtain the information they need more quickly, accurately and intelligently. [0003] In order to meet this demand, th...

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Problems solved by technology

[0005] At present, although the relationship between knowledge can be established through the knowledge graph, users cannot intuitively see the knowledge content contained in the knowl...

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