Data entity relationship extraction method based on deep learning

A data entity and relationship extraction technology, applied in the field of data processing, can solve problems such as poor accuracy, heavy workload, and noisy data in training data, and achieve high accuracy, high efficiency, and improved extraction efficiency and accuracy Effect

Inactive Publication Date: 2019-11-01
福建奇点时空数字科技有限公司
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Problems solved by technology

The supervised learning method manually labels the data, which has high accuracy, but the workload is heavy; the open extraction method is efficient in obtaining training data, but there are more noise data in the obtained training data, and the accuracy is poor

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  • Data entity relationship extraction method based on deep learning
  • Data entity relationship extraction method based on deep learning
  • Data entity relationship extraction method based on deep learning

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Embodiment Construction

[0025] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.

[0026] Such as Figure 1-3 As shown, a method for extracting data entity relationship based on deep learning proposed by the present invention includes the following steps:

[0027] Use the open entity relationship extraction method to obtain training data, use DBPedia, OpenCyc, YAGO or FreeBase entity knowledge base to map data entity relationship instances to a large number of texts in the entity knowledge base, obtain...

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Abstract

The invention discloses a data entity relationship extraction method based on deep learning. The method comprises the following steps: 1, obtaining training data by adopting an open entity relationship extraction method; mapping the data entity relationship instances to a large number of texts in an entity knowledge base by means of a DBPedia, OpenCyc, YAGO or FreeBase entity knowledge base, obtaining training data through a text alignment method, and obtaining training corpora with noise annotations; re-annotating the noise annotation by adopting a supervised entity relationship extraction method, and training a machine learning model on the basis of annotated training data; and extracting a data entity relationship corresponding to the entity pair combination. According to the method, the data entity relationship is extracted by combining the open entity relationship extraction method and the supervised entity relationship extraction method. The training data acquisition efficiency of the open entity relationship extraction method is high. The training data acquired by the supervised entity relationship extraction method is high in accuracy. The extraction efficiency and the accuracy of the entity relationship are improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for extracting data entity relationships based on deep learning. Background technique [0002] Entity Relationship Diagram (Entity Relationship Diagram) directly abstracts the entity type and the relationship between entities from the real world, and then uses the entity relationship diagram (E-R diagram) to represent the data model. It is a practical tool for describing the conceptual world and establishing a conceptual model. This kind of data Models are typically used in the first stages of information system design; eg they are used in the requirements analysis stage to describe information requirements and / or the types of information to be stored in a database. But data modeling techniques can be used to describe any ontology for a particular domain of discourse. In the case of database-based information system design, at a later stage, the conceptual model...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/28G06Q50/26
CPCG06Q50/26G06F16/288
Inventor 肖清林
Owner 福建奇点时空数字科技有限公司
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