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Knowledge extraction and fusion method based on big data

A technology of knowledge extraction and fusion method, applied in the field of knowledge extraction and fusion based on big data, can solve the problems of poor adaptability, lack of entities and relationships, multi-source heterogeneity weak correlation and isolated and scattered big data, etc. Effective integration, improved completeness, increased credibility and effectiveness

Pending Publication Date: 2020-05-22
HUBEI UNIV
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Problems solved by technology

[0003] The problems of timeliness, multi-source heterogeneity, weak correlation and isolated dispersion of these big data have brought great inconvenience to the integration of big data and knowledge extraction. It is difficult to guarantee data completeness and reliability; "heterogeneity" makes the "knowledge extraction" method less adaptable when data is represented and organized by knowledge graphs, and the lack of entities and relationships is more serious; knowledge in knowledge graphs "Dynamic update" makes the maintenance task of the ontology system more and more onerous

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  • Knowledge extraction and fusion method based on big data

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Embodiment Construction

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention.

[0021] refer to figure 1 , a method of knowledge extraction and fusion based on big data, including the following steps:

[0022] S1: Concept extraction, using a domain concept extraction method based on multiple search strategies, performing semantic analysis on the acquired data, automatically obtaining the matching relationship between entities and concepts, and building a candidate concept pool;

[0023] S2: Concept classification relationship extraction, mainly to extract the is-a relationship between concepts;

[0024] S3: Concept non-categorical relationship extraction, mainly extracting non-is-a relationships between concepts;

[0025] S4: Entity alignment ...

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Abstract

The invention belongs to the technical field of big data. The invention relates to a knowledge extraction and fusion method based on big data. Aiming at the problem that big data integration and knowledge extraction are greatly inconvenient due to the characteristics of timeliness, multi-source heterogeneity, weak relevance, isolation dispersity and the like of big data, the following scheme is provided: the method comprises the following steps of concept extraction, concept classification relation extraction, concept non-classification relation extraction, entity alignment and entity linking.According to the invention, for the obtained big data, an entity, relationship and attribute category system of each facet is constructed, syntax meaning analysis is carried out, candidate knowledgepoints are discovered, and then feature selection is conducted, so the knowledge of entity-relation-entity and entity-attribute-attribute value is automatically extracted from mass data, the completeness of big data acquisition is improved, the credibility and validity of the acquired data are improved, high availability, dynamic expansion and updating of a knowledge graph are supported, and effective fusion of big data is realized.

Description

technical field [0001] The invention relates to the field of big data technology, in particular to a method for knowledge extraction and fusion based on big data. Background technique [0002] With the advent of the cloud era, big data has also attracted more and more attention. Some data has the nature of information fragmentation and discreteness, and has many sources, complex types, and various formats. Sparse, so that it has a distinctive feature of "multi-source heterogeneous data, dynamic update of knowledge". [0003] The problems of timeliness, multi-source heterogeneity, weak correlation and isolated dispersion of these big data have brought great inconvenience to the integration of big data and knowledge extraction. It is difficult to guarantee data completeness and reliability; "heterogeneity" makes the "knowledge extraction" method less adaptable when data is represented and organized by knowledge graphs, and the lack of entities and relationships is more seriou...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/35G06F16/31G06F16/901
CPCG06F16/313G06F16/328G06F16/353G06F16/367G06F16/9024
Inventor 曾诚何鹏张马传香王时绘陈昊杨超
Owner HUBEI UNIV
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