中国科学院自动化研究所机构知识库

Knowledge graph, or knowledge base, plays an
important role in a variety of applications in the field of artificial
intelligence. In both research and application of knowledge
graph, knowledge representation learning is one of the fundamen-
tal tasks. Existing representation learning approaches are mainly
based on structural knowledge between entities and relations,
while knowledge among entities per se is largely ignored. Though
a few approaches integrated entity knowledge while learning
representations, these methods lack the flexibility to apply to
multimodalities. To tackle this problem, in this paper, we propose
a new representation learning method, TransAE, by combining
multimodal autoencoder with TransE model, where TransE
is a simple and effective representation learning method for
knowledge graphs. In TransAE, the hidden layer of autoencoder
is used as the representation of entities in the TransE model,
thus it encodes not only the structural knowledge, but also the
multimodal knowledge, such as visual and textural knowledge,
into the final representation. Compared with traditional methods
based on only structural knowledge, TransAE can significantly
improve the performance in the sense of link prediction and
triplet classification. Also, TransAE has the ability to learn
representations for entities out of knowledge base in zero-shot.
Experiments on various tasks demonstrate the effectiveness of
our proposed TransAE method.