Affiliation:
1. School of Digital Media and Design Art Beijing University of Posts and Telecommunications Beijing Key Laboratory of Internet Culture, China
2. Modern Post College Beijing University of Posts and Telecommunications, China
3. School of Digital Media and Design Art Beijing University of Posts and Telecommunications, China
Abstract
As a combination of information computing technology and the cultural field, cultural computing is gaining more and more attention. The knowledge graph is also gradually applied as a particular data structure in the cultural area. Based on the domain knowledge graph data of the Beijing Municipal Social Science Project ”Mining and Utilization of Cultural Resources in the Ancient Capital of Beijing,” this paper proposes a graph representation learning model CR-TransR that integrates cultural attributes. Through the analysis of the data in the cultural field of the ancient capital of Beijing, a cultural feature dictionary is constructed, and a domain-specific feature matrix is constructed in the form of word vector splicing. The feature matrix is used to constrain the embedding graph model TransR, and then the feature matrix and the TransR model are jointly trained to complete the embedded expression of the knowledge graph. Finally, a comparative experiment is carried out on the Beijing ancient capital cultural knowledge graph dataset and the effects of the classic graph embedding algorithms TransE, TransH, and TransR. At the same time, we try to reproduce the embedding method with the core idea of neighbor node information aggregation as the core idea, and CRTransR are compared. The experimental tasks include link prediction and triplet classification, and the experimental results show that the CRTransR model performs better.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design,Computer Science Applications,Information Systems,Conservation
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