Knowlege Graph Embedding by Flexible Translation
This work solves scalability and efficiency issues in knowledge graph completion for AI applications, but it is incremental as it builds on existing translation-based methods.
The paper tackles the problem of knowledge graph embedding by addressing limitations in handling complex relation types like reflexive and many-to-many relations, proposing TransF with flexible translation, and shows it improves performance on benchmark datasets.
Knowledge graph embedding refers to projecting entities and relations in knowledge graph into continuous vector spaces. State-of-the-art methods, such as TransE, TransH, and TransR build embeddings by treating relation as translation from head entity to tail entity. However, previous models can not deal with reflexive/one-to-many/many-to-one/many-to-many relations properly, or lack of scalability and efficiency. Thus, we propose a novel method, flexible translation, named TransF, to address the above issues. TransF regards relation as translation between head entity vector and tail entity vector with flexible magnitude. To evaluate the proposed model, we conduct link prediction and triple classification on benchmark datasets. Experimental results show that our method remarkably improve the performance compared with several state-of-the-art baselines.