Open AccessThe Distributed Representation of Knowledge Graphs Based on Pseudo-Siamese Network
Author(s) -
Wei Zhuo,
Ye Zhang,
Fan Wang,
Shuai Liu
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/440/2/022012
Subject(s) - relation (database) , embedding , feature (linguistics) , constraint (computer aided design) , representation (politics) , abstraction , similarity (geometry) , computer science , simple (philosophy) , feature vector , theoretical computer science , space (punctuation) , vector space , inverse , artificial intelligence , mathematics , algorithm , data mining , pure mathematics , image (mathematics) , geometry , linguistics , philosophy , epistemology , politics , political science , law , operating system
This paper proposes to transform the (head entity, relation) and tail entity into the same feature space through the Pseudo-Siamese network, and calculate the similarity between the two parts in this feature space, embedding vector of entity and relation have been optimization for constraint conditions.In this paper, the triple is regarded as the abstraction of the answer pair in the factual simple question-answering. According to the corresponding relation of the answer, the corresponding relation between the (head entity, relation) and the tail entity in the triple is obtained, and the constraint characteristics of the elements in the triple are modeled. And then by constructing inverse relations to build a new triple, thus the number of training samples is expanded to improve the learning results of the model.