  Recognizing relations between two words is a fundamental task with the broad
applications. Different from extracting relations from text, it is difficult to
identify relations among words without their contexts. Especially for long-tail
relations, it becomes more difficult due to inadequate semantic features.
Existing approaches based on language models (LMs) utilize rich knowledge of
LMs to enhance the semantic features of relations. However, they capture
uncommon relations while overlooking less frequent but meaningful ones since
knowledge of LMs seriously relies on trained data where often represents common
relations. On the other hand, long-tail relations are often uncommon in
training data. It is interesting but not trivial to use external knowledge to
enrich LMs due to collecting corpus containing long-tail relationships is
hardly feasible. In this paper, we propose a sememe knowledge enhanced method
(SememeLM) to enhance the representation of long-tail relations, in which
sememes can break the contextual constraints between wors. Firstly, we present
a sememe relation graph and propose a graph encoding method. Moreover, since
external knowledge base possibly consisting of massive irrelevant knowledge,
the noise is introduced. We propose a consistency alignment module, which
aligns the introduced knowledge with LMs, reduces the noise and integrates the
knowledge into the language model. Finally, we conducted experiments on word
analogy datasets, which evaluates the ability to distinguish relation
representations subtle differences, including long-tail relations. Extensive
experiments show that our approach outperforms some state-of-the-art methods.
