  Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding
entities and relations into continuous vector spaces. Existing methods are
mainly structure-based or description-based. Structure-based methods learn
representations that preserve the inherent structure of KGs. They cannot well
represent abundant long-tail entities in real-world KGs with limited structural
information. Description-based methods leverage textual information and
language models. Prior approaches in this direction barely outperform
structure-based ones, and suffer from problems like expensive negative sampling
and restrictive description demand. In this paper, we propose LMKE, which
adopts Language Models to derive Knowledge Embeddings, aiming at both enriching
representations of long-tail entities and solving problems of prior
description-based methods. We formulate description-based KE learning with a
contrastive learning framework to improve efficiency in training and
evaluation. Experimental results show that LMKE achieves state-of-the-art
performance on KE benchmarks of link prediction and triple classification,
especially for long-tail entities.
