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Deep learning method based on map topological structure and entity text description

A technology of deep learning and map topology, applied in special data processing applications, instruments, electrical digital data processing, etc., to achieve the effect of solving the problem of knowledge map completion in the open world

Inactive Publication Date: 2018-10-19
SUN YAT SEN UNIV
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Problems solved by technology

[0005] The present invention provides a deep learning method based on map topology and entity text description to solve the ability of knowledge map completion

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  • Deep learning method based on map topological structure and entity text description
  • Deep learning method based on map topological structure and entity text description
  • Deep learning method based on map topological structure and entity text description

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Embodiment 1

[0022] The present invention proposes a deep learning method based on map topology and entity text description. This method is based on deep learning theory. On the one hand, in the entity text information processing, an attention mechanism is added, and a circular convolution network is introduced to process text, which can The descriptive text information of entities in the knowledge graph is more fully utilized. On the other hand, the rich information contained in the topological structure of the knowledge graph itself is mined to improve the model's ability to detect incomplete triples or in the "?" prediction accuracy, and with the continuous addition of correctly predicted triples, the topology of the knowledge map will become more complex, and the information that can be provided will be more abundant, so that The model's ability to solve knowledge graph completion will also be more powerful.

[0023] Generally speaking, the model is divided into a joint model of two ...

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Abstract

The invention provides a deep learning method based on a map topological structure and entity text description. In solving the problem of knowledge map completion, the entity to be completed may already exist in the knowledge map (need to be discovered), or may not be in the knowledge map (need to be generated). For the task that needs to be discovered, it can be regarded as a knowledge map completion problem in a closed environment, and the model M1 can 'discover' the entity well; for the tasks that need to be discovered, the model M2 can fully explore the text information with the help of the attention mechanism and the circular convolution network, providing a powerful guarantee for 'generating' the entity. The combination of these two sub-models can solve the problem of complementing the knowledge map of the open world.

Description

technical field [0001] The present invention relates to the field of text processing algorithms, more specifically, to a deep learning method based on map topology and entity text description. Background technique [0002] Knowledge graph is a research hotspot in the current big data era. Since Google launched its first version of knowledge graph in 2012, it has set off a wave of enthusiasm in academia and industry. In the research of knowledge graph, the problem of knowledge graph completion (Knowledge Graph Completion) occupies an extremely important position. The goal of knowledge map completion is to complete the existing incomplete knowledge map as much as possible, so as to enrich the information contained in the knowledge map. [0003] At present, related technologies on knowledge map completion include: knowledge map completion based on crowdsourcing, semantic deep learning model based on knowledge map topology, reasoning model based on relational reasoning rules, e...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 卓汉逵荣二虎
Owner SUN YAT SEN UNIV
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