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.

Vector representation generation method, apparatus and device for knowledge graph

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.

Medical text translation method and device and storage medium

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.

Text processing method and device based on natural language and knowledge graph

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.

Wu Hua

Papers

Text processing method and device based on natural language and knowledge graph

Knowledge graph vector representation generation method, device and equipment

Vector representation generation method, apparatus and device for knowledge graph