Resource storage efficiency optimization method used in drivers and oriented by data graphs, information graphs and knowledge graphs

Abstract

The invention discloses a resource storage efficiency optimization method used in drivers and oriented by data graphs, information graphs and knowledge graphs, provides resource forms including paraphrases of data, information, knowledge and the like, and concept representations of the data graphs, information graphs and knowledge graphs, and belongs to the technical crossing field of distributed computation and software engineering. Taking into account two aspects including combination of experiment and knowledge to deal with an autoincrement mode, and less interaction burdens of specialists, it is unable to judge which layer of graphs should be used for resource storage merely according to the types of resources. According to the resource storage efficiency optimization method, by means of calculation of storage costs and user input quantity, resource storage frameworks in different types are equitably distributed, and the storage costs are reduced, and meanwhile, the equitably distributed resource storage frameworks facilitates the improvement of resource searching efficiency.

Description

technical field [0001] The present invention is an investment-driven resource storage efficiency optimization method for data graphs, information graphs and knowledge graphs, which determines which layer of graph storage architecture should store resources in the form of data, information, and knowledge according to the storage cost . It is mainly used to model and store resources that appear in the form of data, information, and knowledge through data graphs, information graphs, and knowledge graphs. It belongs to the interdisciplinary field of distributed computing and software engineering technologies. Background technique [0002] With the rapid development of global informatization, the amount of resources on the Internet has increased dramatically. The unreasonable organization and storage of resources leads to the decline of user retrieval efficiency. Traditional search engine technology quickly retrieves and sorts web resources according to user query requirements....

AI Technical Summary

Problems solved by technology

The unreasonable organization and storage of resources leads to the decline of user retrieval efficiency

Traditional search engine technology quickly retrieves and sorts web resources according to user query requirements. For a large number of resources fed back by search engines, users need to manually check and filter, which is difficult to meet the needs of users to fully control resources.

Existing techniques including machine learning-based approaches and ontology-based approaches have made many contributions to address this challenge, however, machine learning-based approaches lack effective mechanisms to explicitly incorporate empirical knowledge with trained models, Ontology-based approaches are limited by the heavy burden on the part of human experts

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