  Entity images could provide significant visual information for knowledge
representation learning. Most conventional methods learn knowledge
representations merely from structured triples, ignoring rich visual
information extracted from entity images. In this paper, we propose a novel
Image-embodied Knowledge Representation Learning model (IKRL), where knowledge
representations are learned with both triple facts and images. More
specifically, we first construct representations for all images of an entity
with a neural image encoder. These image representations are then integrated
into an aggregated image-based representation via an attention-based method. We
evaluate our IKRL models on knowledge graph completion and triple
classification. Experimental results demonstrate that our models outperform all
baselines on both tasks, which indicates the significance of visual information
for knowledge representations and the capability of our models in learning
knowledge representations with images.
