Cross-domain knowledge graph construction method and device based on artificial intelligence

Abstract

The invention provides a cross-domain knowledge graph construction method and device based on artificial intelligence, and relates to the field of artificial intelligence and cross-domain knowledge graph construction, and the device comprises a knowledge model which is used for describing a concept hierarchy system and is a concept template of knowledge in a knowledge base; a multi-source heterogeneity, data source management which supports multi-source heterogeneity, and comprises management of relational data sources, distributed Hdfs, Hbase, Hive and mongoDB data sources; knowledge data extraction, so that a user carries out data extraction on data of different data source types to form a series of high-quality fact expressions, and a foundation is laid for construction of an upper-layer mode layer; and cross-domain ontology fusion, and cross-domain ontology fusion can be carried out according to data of various industry fields, so that high quality of atlas data is always kept. Andmore accurate and reliable situation description in a fusion scene is obtained.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and knowledge graph, and in particular relates to an artificial intelligence-based cross-domain knowledge graph construction method and device. Background technique [0002] With the introduction and innovation of big data technology, artificial intelligence, knowledge map technology, etc., an era of large-scale model production, analysis and application of data has opened. Data in data sources are useful because they model parts of the real world and its subjects. It has become a major trend to build industry domain models with the help of knowledge graphs. Existing industry domain knowledge graphs are usually constructed manually, lacking a unified construction method, and are all built for specific industry domains. Therefore, the scope of their description is extremely limited. . Moreover, most multi-source heterogeneous data fusion does not pay attention to the rich semantic informati...

AI Technical Summary

Problems solved by technology

It has become a major trend to build industry domain models with the help of knowledge graphs. Existing industry domain knowledge graphs are usually constructed manually, lacking a unified construction method, and are all built for specific industry domains. Therefore, the scope of their description is extremely limited.

Moreover, most multi-source heterogeneous data fusion does not pay attention to the rich semantic information contained in the semantic level of the data itself, which will lead to the loss of more semantic information as the fusion level increases.

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