Building method and system used for knowledge obtaining model in knowledge graph

A technology of knowledge graph and knowledge acquisition, applied in the direction of biological neural network model, neural architecture, special data processing applications, etc., can solve the problems of low performance of knowledge acquisition, and achieve improved knowledge acquisition performance, good practicability, and improved stability Effect

Active Publication Date: 2018-09-21
TSINGHUA UNIV
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Problems solved by technology

[0005] In order to solve the problem of low knowledge acquisition performance in the existing knowledge graph knowledge acquisition model, which only con

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  • Building method and system used for knowledge obtaining model in knowledge graph
  • Building method and system used for knowledge obtaining model in knowledge graph
  • Building method and system used for knowledge obtaining model in knowledge graph

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Embodiment Construction

[0046] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0047] refer to figure 1 , figure 1 It is a flow chart of a method for building knowledge or model acquisition in a knowledge map provided by an embodiment of the present invention, the method includes:

[0048] S1, constructing a first training set consisting of a plurality of sentences as input data, and the relationship between any two entities in each sentence in the knowledge map as classification results, and using the first training set to perform the first neural network training, and obtaining a vector of the input data of the first training set converted in the first neural network. Each sentence in the training set contains at least 2 entities.

[0049] Spec...

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Abstract

The invention provides a building method used for a knowledge obtaining model in a knowledge graph. The method comprises the steps of constructing a first training set consisting of multiple text sentences as input data and a relationship between any two entities in each sentence in the knowledge graph, as a classification result, and training a first neural network; constructing a second trainingset consisting of triples in multiple knowledge graphs, and training a second neural network; by taking input data vectors obtained in the second neural network as attention features of the first neural network, building a relationship extraction model; by taking input data vectors obtained in the first neural network as attention features of the second neural network, building a knowledge representation model; and fusing the relationship extraction model and the knowledge representation model to obtain the knowledge obtaining model in the knowledge graph. According to the method provided bythe invention, the two task models of knowledge representation and relationship extraction are integrated at the same time, and the features of the knowledge graph and free texts can be comprehensively extracted, so that the model stability and accuracy are improved.

Description

technical field [0001] The present invention relates to the field of information processing, and more specifically, to a method and system for constructing a knowledge acquisition model in a knowledge graph. Background technique [0002] Knowledge graph, also known as knowledge base in some scenarios, is a knowledge system formed after structuring human knowledge in the real world. In a knowledge graph, a large amount of knowledge, such as information in open databases and encyclopedias, is usually expressed in the form of relational data sets. In a relational data set, basic facts are abstracted as entities, and related information such as rules, logic, and reasoning are abstracted as relationships between entities. If entities correspond to points and relationships correspond to edges, then these knowledge can be further presented in the form of graphs, so that they can be efficiently used by computers, and this is the significance of studying knowledge graphs. This mode...

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Application Information

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IPC IPC(8): G06F17/30G06N3/04
CPCG06N3/045
Inventor 刘知远韩旭孙茂松
Owner TSINGHUA UNIV
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