Knowledge graph construction method fusing inference engine

Abstract

The invention relates to a knowledge graph construction method fusing an inference engine, and belongs to the field of knowledge graphs. The method comprises the following steps: manually constructing a knowledge graph schema according to the actual condition of a service, accelerating the definition of entity attributes and relationships by utilizing an inference engine, importing semi-structured data to perform knowledge extraction, and cleaning and normalizing and disambiguating knowledge graph data; and then integrating rule design to carry out deep reasoning processing on knowledge to form a final rule knowledge base. According to the method, mass information is integrated and managed based on the structure data of the knowledge graph, rule reasoning is combined, the knowledge reasoning effect can be improved, and a more accurate reasoning result and visual display are obtained. According to the knowledge graph, multi-source heterogeneous data can be fused, the organization and relevance of the data are enhanced, and mass data can be conveniently stored and managed; wherein, an inference engine is introduced in the construction process of the knowledge graph to carry out rule design, so that deep inference and knowledge integration are effectively carried out on knowledge, the construction process of the knowledge graph is accelerated, and the data accuracy is improved.

Description

technical field [0001] The invention belongs to the field of knowledge graphs, and in particular relates to a method for constructing a knowledge graph of a fusion reasoning machine. Background technique [0002] In the era of big data, enterprises or fields have massive amounts of data, and there are different types of heterogeneous data such as structured, semi-structured, and unstructured. It is stored in different databases and file systems, so the association and fusion of various data is poor, data extraction and analysis are difficult, and knowledge representation cannot be well performed. The emergence of knowledge graph technology, which can build a large-scale semantic network of entities and relationships, and support intelligent representation, reasoning, retrieval and service of knowledge, has become an important way to solve traditional computing technology. [0003] As one of the fields of artificial intelligence technology, knowledge graph describes entities...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is how to provide a method for building a knowledge map that integrates reasoning machines to solve the problem that reasoning machines are almost powerless for operations such as association integration of unstructured and semi-structured data, knowledge extraction, and analysis and mining. The machine is not clear and intuitive enough in the visual display of data results

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