Knowledge Graphs (KGs) have found many applications in industrial and academic settings, which turn, motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among entities alr...

Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) an efficient and scalable manner. The key to success of these is their ability learn low-rank vector representations entities relations. Despite the rapid development KGE models, state-of-the-art approaches mostly focused on new ways represent embeddings interaction fun...

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure graph. This then enables inference graph properties. Existing techniques, however, do not scale well to large graphs. While several techniques using compute clusters have been proposed, they require continuous communication between nodes and cannot handle node failure. We therefore propose...

This paper presents a framework for learning player embeddings in competitive games and events. Players their win-loss relationships are modeled as skill gap graph, which is an undirected weighted graph. The learned from the graph using random walk-based embedding method can reflect relative levels among players. Embeddings low-dimensional vector representations that be conveniently applied to ...

A key to knowledge graph embedding (KGE) is choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose unified perspective of introduce uncertainty into KGE from the view group theory. Our model can incorporate existing models (i.e., generality), ensure computation tractable efficiency) enjoy expressive power random variables expre...