  Due to the incompleteness of knowledge graphs (KGs), zero-shot link
prediction (ZSLP) which aims to predict unobserved relations in KGs has
attracted recent interest from researchers. A common solution is to use textual
features of relations (e.g., surface name or textual descriptions) as auxiliary
information to bridge the gap between seen and unseen relations. Current
approaches learn an embedding for each word token in the text. These methods
lack robustness as they suffer from the out-of-vocabulary (OOV) problem.
Meanwhile, models built on character n-grams have the capability of generating
expressive representations for OOV words. Thus, in this paper, we propose a
Hierarchical N-Gram framework for Zero-Shot Link Prediction (HNZSLP), which
considers the dependencies among character n-grams of the relation surface name
for ZSLP. Our approach works by first constructing a hierarchical n-gram graph
on the surface name to model the organizational structure of n-grams that leads
to the surface name. A GramTransformer, based on the Transformer is then
presented to model the hierarchical n-gram graph to construct the relation
embedding for ZSLP. Experimental results show the proposed HNZSLP achieved
state-of-the-art performance on two ZSLP datasets.
