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Author

Zhu Yong

Bio: Zhu Yong is an academic researcher. The author has contributed to research in topics: Context model & Context (language use). The author has an hindex of 1, co-authored 4 publications receiving 2 citations.

Papers
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Patent
24 Jan 2020
TL;DR: In this paper, a text processing method and device based on a natural language and a knowledge graph is described, which relates to the technical field of artificial intelligence, and an electronic device uses semantic representation obtained by a joint learning model, which is obtained by combining knowledge graph representation learning and natural language representation learning.
Abstract: The invention discloses a text processing method and device based on a natural language and a knowledge graph, and relates to the technical field of artificial intelligence. According to the specificimplementation scheme, an electronic device uses semantic representation obtained by a joint learning model, the joint learning model is obtained by combining knowledge graph representation learning and natural language representation learning and combines knowledge graph learning representation and natural language learning representation; compared with semantic representation of learning a prediction object only through knowledge graph representation learning or natural language representation learning, the joint learning model considers more and more comprehensive factors, and therefore theaccuracy of semantic representation can be improved, and the accuracy of text processing is improved.

1 citations

Patent
14 Feb 2020
TL;DR: In this article, a knowledge graph vector representation generation method is proposed for the field of artificial intelligence. And the method comprises the steps: obtaining a knowledge graphs, and enabling the knowledge graph to comprise a plurality of entity nodes; obtaining a context type and context data corresponding to the knowledge graphs; and generating vector representations corresponding to entity nodes through a context model according to the context data and the context type.
Abstract: The invention provides a knowledge graph vector representation generation method, device and equipment, and relates to the technical field of artificial intelligence, and the specific implementation scheme is that the method comprises the steps: obtaining a knowledge graph, and enabling the knowledge graph to comprise a plurality of entity nodes; obtaining a context type and context data corresponding to the knowledge graph; and generating vector representations corresponding to the plurality of entity nodes through a context model according to the context data and the context type. Therefore,finer semantic representation of the entity in the context is realized, and the knowledge graph representation learning accuracy is further improved.

1 citations

Patent
15 Apr 2021
TL;DR: In this paper, a vector representation generation method, apparatus and device for a knowledge graph, wherein same relate to the technical field of artificial intelligence, is proposed, where the knowledge graph comprises a plurality of entity nodes.
Abstract: A vector representation generation method, apparatus and device for a knowledge graph, wherein same relate to the technical field of artificial intelligence. The specific implementation solution is: acquiring a knowledge graph, wherein the knowledge graph comprises a plurality of entity nodes (101); acquiring a context type and context data corresponding to the knowledge graph (102); and generating, according to the context type and the context data and by means of a context model, a vector representation corresponding to the plurality of entity nodes (103). Thus, finer semantic representation of an entity in context is realized, thereby further improving the accuracy of knowledge graph representation learning.
Patent
Feng Zhifan1, Wang Haifeng, Ren Kexin, Zhu Yong, Lv Yajuan 
06 Apr 2021
TL;DR: In this paper, a multimodal content processing method, apparatus, device and storage medium, which relate to the technical field of artificial intelligence, is presented, where the specific implementation is: receiving a content processing request of a user which is configured to request semantic understanding of multimodAL content to be processed, analyzing the multimmodal content to obtain the multim-odal knowledge nodes corresponding to the multim modal content, determining a semantic understanding result of the multimODal content according to the multidimensional knowledge nodes, a pre-constructed multimodality knowledge graph and
Abstract: The present disclosure discloses a multimodal content processing method, apparatus, device and storage medium, which relate to the technical field of artificial intelligence. The specific implementation is: receiving a content processing request of a user which is configured to request semantic understanding of multimodal content to be processed, analyzing the multimodal content to obtain the multimodal knowledge nodes corresponding to the multimodal content, determining a semantic understanding result of the multimodal content according to the multimodal knowledge nodes, a pre-constructed multimodal knowledge graph and the multimodal content, the multimodal knowledge graph including: the multimodal knowledge nodes and an association relationship between multimodal knowledge nodes. The technical solution can obtain an accurate semantic understanding result, realize an accurate application of multimodal content, and solve the problem in the prior art that multimodal content understanding is inaccurate.

Cited by
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Patent
15 Apr 2021
TL;DR: In this paper, a vector representation generation method, apparatus and device for a knowledge graph, wherein same relate to the technical field of artificial intelligence, is proposed, where the knowledge graph comprises a plurality of entity nodes.
Abstract: A vector representation generation method, apparatus and device for a knowledge graph, wherein same relate to the technical field of artificial intelligence. The specific implementation solution is: acquiring a knowledge graph, wherein the knowledge graph comprises a plurality of entity nodes (101); acquiring a context type and context data corresponding to the knowledge graph (102); and generating, according to the context type and the context data and by means of a context model, a vector representation corresponding to the plurality of entity nodes (103). Thus, finer semantic representation of an entity in context is realized, thereby further improving the accuracy of knowledge graph representation learning.
Patent
17 Nov 2020
TL;DR: In this article, a medical text translation method and device and a storage medium is described, which consists of the steps of obtaining a to-be-translated medical text, performing semantic feature extraction on the to- betranslated text to obtain a first feature vector; obtaining a target feature vector corresponding to the text, wherein the target vector is used for representing a medical knowledge graph.
Abstract: The invention relates to the field of medical science and technology, and particularly discloses a medical text translation method and device and a storage medium. The method comprises the steps of obtaining a to-be-translated medical text; performing semantic feature extraction on the to-be-translated medical text to obtain a first feature vector; obtaining a target feature vector corresponding to the to-be-translated medical text, wherein the target feature vector is used for representing a medical knowledge graph corresponding to the to-be-translated medical text; splicing the first featurevector and the target feature vector to obtain a second feature vector; and translating the to-be-translated medical text according to the second feature vector. The embodiment of the invention is beneficial to improving the accuracy of medical text translation.