Embedding of Hierarchically Typed Knowledge Bases


  • Richong Zhang Beihang University
  • Fanshuang Kong Beihang University
  • Chenyue Wang Beihang University
  • Yongyi Mao University of Ottawa


Knowledge Base Completion, Embedding, Entity Types


Embedding has emerged as an important approach to prediction, inference, data mining and information retrieval based on knowledge bases and various embedding models have been presented. Most of these models are "typeless," namely, treating a knowledge base solely as a collection of instances without considering the types of the entities therein. In this paper, we investigate the use of entity type information for knowledge base embedding. We present a framework that augments a generic "typeless" embedding model to a typed one. The framework interprets an entity type as a constraint on the set of all entities and let these type constraints induce isomorphically a set of subsets in the embedding space. Additional cost functions are then introduced to model the fitness between these constraints and the embedding of entities and relations. A concrete example scheme of the framework is proposed. We demonstrate experimentally that this framework offers improved embedding performance over the typeless models and other typed models.




How to Cite

Zhang, R., Kong, F., Wang, C., & Mao, Y. (2018). Embedding of Hierarchically Typed Knowledge Bases. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11548



AAAI Technical Track: Knowledge Representation and Reasoning