Knowledge-driven business operation graph construction method

Abstract

The invention provides a knowledge-driven business operation graph construction method. The knowledge-driven business operation graph construction method comprises two parts of knowledge generation and domain knowledge graph construction based on multi-source heterogeneous business data and whole-network domain operation graph network construction based on operation mode evolution. Knowledge generation and domain knowledge graph construction based on multi-source heterogeneous business data comprise knowledge extraction and association processing based on multi-mode full-link multi-dimensionalbusiness data, collaborative modeling and graph generation based on multi-source extraction knowledge and knowledge graph expansion based on business domain knowledge autonomous discovery. Accordingto the method, the construction problem of the business operation atlas is converted into the problem of searching related operation knowledge atlas contents from the business data knowledge atlas according to the business operation process and the required data, so that the subgraph query problem of related operation businesses is searched from the huge public business data knowledge atlas.

Description

technical field [0001] The invention belongs to the technical field of knowledge extraction and related technology and business fusion, and in particular relates to a knowledge-driven business operation map construction method. Background technique [0002] The development of artificial intelligence has gone through the stage from machine intelligence to perceptual intelligence, and is moving towards the stage of cognitive intelligence. But to achieve cognitive intelligence, machines must learn to deal with the complex language of humans and learn to reason about knowledge. This is a major problem encountered by artificial intelligence. At present, the combination of machine deep learning and knowledge map reasoning can well solve the semantic gap of human natural language. [0003] Knowledge graph is a very large-scale semantic network system, its main purpose is to describe the relationship between entities or concepts in the real world. Through the collection of a large...

AI Technical Summary

Problems solved by technology

[0004] (1) Many existing algorithms do not consider the matching order of the vertices and edges of the query graph, and simply match according to the array labels of the vertices, so that the mismatch in the structure cannot be found earlier, resulting in redundant search space

[0005] (2) Even small-scale query subgraphs may lead to large query overhead