Character relation visual query method and system based on knowledge graph

Abstract

The invention relates to a character relation visual query method and system based on a knowledge graph. The method comprises the following steps: crawling webpage information related to a current character from the Internet by using a Scrapy framework; analyzing the webpage information, and extracting character information and relation information related to the current character; normalizing thecharacter information and the relation information, and constructing quintuple data of all characters related to the current character; performing character relation backward reasoning on the quintuple data to obtain backward quintuple data; constructing a character relation knowledge graph according to the quintuple data, and storing the character relation knowledge graph in a graph database; and obtaining a character relation knowledge graph corresponding to the to-be-queried character from the graph database and carrying out visual display. The character relation knowledge graph is storedin the graph database, so that the query speed is increased. In addition, knowledge in the graph is displayed through a visualization technology, a sense of visual impact is generated, and the character relation can be better understood.

Description

technical field [0001] The present invention relates to the technical field of natural language processing of artificial intelligence, in particular to a method and system for visual querying of character relationships based on knowledge graphs. Background technique [0002] With the development of society and science and technology, the Internet industry is also booming, which leads to the rapid growth of data. At present, with the maturity of various search engines, people can quickly obtain a lot of information they want on the Internet. However, a large amount of information on the Internet is hidden in semi-structured web pages or unstructured texts. Faced with huge data, people need to extract the required knowledge and understand it by themselves. When faced with abstract data, people cannot intuitively see or query the information they want, and people expect to use other methods to more intuitively observe and obtain the knowledge they need. Therefore, an intellig...

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