Visualization of Knowledge Graphs with Embeddings: an Essay on Recent Trends and Methods
원문 초록
In this essay we discuss the recent trends in visual analysis and exploration of Knowledge Graphs, particularly in conjunction with Knowledge Graph Embedding techniques. We present an overview of the current state of visualization techniques and frameworks for KGs, in relation to four identified challenges. The challenges in visualizing Knowledge Graphs include the need for intuitive and modular interfaces, performance in handling big data, and difficulties for users in understanding and using query languages. We find frameworks that generally satisfy the intuitive UI, performance, and query support requirements, but few satisfying the modularity requirement. In the context of Knowledge Graph Embeddings, we divide the approaches that use embeddings to facilitate exploration of Knowledge Graphs from those that aim at the explanation of the embeddings themselves. We find significant differences between the two perspectives. Finally, we highlight some possible directions for future work, including diffusion of the unmet requirements, implementation of new visual features, and experimentation with relation visualization as a peculiar element of Knowledge Graphs.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs
Embedding-Based Entity Alignment Using Relation Structural Similarity
Knowledge Graph Embedding via Graph Attenuated Attention Networks
Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings
토론
이 논문과 연결된 포럼 글이 아직 없습니다.