A mass knowledge resource storage method based on a knowledge graph

A technology of knowledge graph and knowledge, which is applied in the field of storage of massive knowledge resources based on knowledge graph, and can solve problems such as no stored procedure language, no support for hot backup, programmer restrictions, etc.
CN109726296APending Publication Date: 2019-05-07武汉理工数字传播工程有限公司
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
武汉理工数字传播工程有限公司
Publication Date
2019-05-07

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Abstract

The invention discloses a mass knowledge resource storage method based on a knowledge graph. The mass knowledge resource storage method comprises: establishing a knowledge map meta-model by a tripletof an entity-relationship-entity manner;; Establishing a knowledge resource ontology model by taking the knowledge graph model layer, the knowledge resource ontology layer and the knowledge resource instance layer as hierarchical structures; And constructing a knowledge resource ontology description model by using the knowledge resource ontology entity and the knowledge resource ontology model through an instance mapping relationship to form corresponding knowledge graph storage. According to the technical scheme, the storage space can be saved, and the knowledge resource processing efficiencycan be improved.
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Description

technical field

[0001] The invention relates to the field of storage technology, in particular to a method for storing massive knowledge resources based on knowledge graphs. Background technique

[0002] Existing storage methods for massive heterogeneous knowledge resources include: ① NoSQL derived databases such as HBase, Hadoop, and MapReduce. However, none of these databases can cover all application scenarios, and their versatility is not high in the face of massive heterogeneous data from publishing houses. ②Ontology-based storage scheme. Ontology-based storage schemes include text storage, database storage, and so on. The text storage method has a simple storage form and is suitable for small-scale ontology. The database storage method will have high information redundancy when saving ontology instances, and the query speed is slow. Traditional storage technology and fast retrieval technology are difficult to meet the needs of massive heterogeneous data storage and...

Claims

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