Representing Hierarchical Structure by Using Cone Embedding
원문 초록
Graph embedding is becoming an important method with applications in various areas, including social networks and knowledge graph completion. In particular, Poincar\'e embedding has been proposed to capture the hierarchical structure of graphs, and its effectiveness has been reported. However, most of the existing methods have isometric mappings in the embedding space, and the choice of the origin point can be arbitrary. This fact is not desirable when the distance from the origin is used as an indicator of hierarchy, as in the case of Poincar\'e embedding. In this paper, we propose cone embedding, embedding method in a metric cone, which solve these problems, and we gain further benefits: 1) we provide an indicator of hierarchical information that is both geometrically and intuitively natural to interpret, and 2) we can extract the hierarchical structure from a graph embedding output of other methods by learning additional one-dimensional parameters.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion
CausalLP: Learning causal relations with weighted knowledge graph link prediction
QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion
토론
이 논문과 연결된 포럼 글이 아직 없습니다.