TransI: Translating Infinite Dimensional Embeddings Based on Trend Smooth Distance

Knowledge representation learning aims to transform entities and relationships in a knowledge base into computable forms, so that an efficient calculation can be realized. It is of great significance to the construction, reasoning and application of knowledge base. The traditional translation-based models mainly obtain the finite dimension vector representation of entities or relationships by projecting to finite dimensional Euclidean space. These simple and effective methods greatly improve the efficiency and accuracy of knowledge representation. However, they ignore a fact that the semantic space develops and grows forever with the passage of time. Finite dimensional Euclidean space is not enough in capacity for vectorizing infinitely growing semantic space. Time is moving forward forever, so knowledge base would expand infinitely with time. This determines that the vector representation of entities and relationships should support infinite capacity. We fill the gap by putting forward TransI (Translating Infinite Dimensional Embeddings) model, which extends knowledge representation learning from finite dimensions to infinite dimensions. It is trained by Trend Smooth Distance based on the idea of continuous infinite dimension vector representation. The Training Efficiency of TransI model is obviously better than TransE under the same setting, and its effect of Dimension Reduction Clustering is more obvious.