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Knowledge graph representation learning method and system in combination with entity description

A knowledge graph and entity technology, applied in the field of knowledge graph representation learning methods and systems, can solve problems such as failure to fully utilize entity description information and inability to represent new entities, and achieve good practicability and high accuracy.

Active Publication Date: 2017-06-23
TSINGHUA UNIV
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AI Technical Summary

Problems solved by technology

[0005] An object of the present invention is to solve the following technical problem: how to provide a new knowledge map representation learning method combined with entity description, and efficiently and accurately complete the representation learning of knowledge graphs, so as to overcome the inability of existing technologies to represent new entities, and the lack of Problems that can make full use of entity description information

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  • Knowledge graph representation learning method and system in combination with entity description
  • Knowledge graph representation learning method and system in combination with entity description
  • Knowledge graph representation learning method and system in combination with entity description

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Embodiment Construction

[0040] In order to understand the above-mentioned purpose, features and advantages of the present invention more clearly, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0041] Firstly, the basic idea of ​​the present invention and the basic concepts involved therein are explained.

[0042] The knowledge graph representation learning method aims to map all entities and relationships into a low-dimensional continuous vector space, and use vectors to represent entities and relationships, which solves the sparsity problem in knowledge graph learning. A knowledge map representation learning method combined with entity description proposed by the present invention can make full use of entity text description information to enhance ...

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Abstract

The invention provides a knowledge graph representation learning method and system in combination with entity description. According to the method and the system, a continuous bag-of-words-based model and a convolutional neural network-based model are proposed for constructing description-based vector representation of entities; not only triple relationship information among the entities but also text information contained in entity description are utilized, and two entity vector representation modes obtained by model learning are used, so that higher accuracy can be obtained in tasks of knowledge graph complementation, entity classification and the like; and the description-based vector representation constructs entity vectors through the text information, can well represent new entities or entities inexistent in a training set, and has high practicality.

Description

technical field [0001] The present invention relates to the fields of natural language processing and knowledge graphs, in particular to a knowledge graph representation learning method and system combined with entity description. Background technique [0002] With the rapid development of society, we have entered the era of information explosion, and a large number of new entities and information are generated every day. As the most convenient information acquisition platform today, the Internet has increasingly urgent needs for effective information screening and induction. How to obtain valuable information from massive data has become a difficult problem. This is where the knowledge graph comes into being. [0003] The knowledge graph represents all the proper nouns and things such as people, place names, book titles, and team names in the world as entities, and represents the internal connections between entities as relationships, aiming to represent the massive knowle...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/288
Inventor 孙茂松谢若冰刘知远栾焕博刘奕群马少平
Owner TSINGHUA UNIV
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