Solving Hard Analogy Questions with Relation Embedding Chains
Nitesh Kumar Steven Schockaert
Modelling how concepts are related is a central topic in Lexical Semantics. A
common strategy is to rely on knowledge graphs (KGs) such as ConceptNet, and to
model the relation between two concepts as a set of paths. However, KGs are
limited to a fixed set of relation types, and they are incomplete and often
noisy. Another strategy is to distill relation embeddings from a fine-tuned
language model. However, this is less suitable for words that are only
indirectly related and it does not readily allow us to incorporate structured
domain knowledge. In this paper, we aim to combine the best of both worlds. We
model relations as paths but associate their edges with relation embeddings.
The paths are obtained by first identifying suitable intermediate words and
then selecting those words for which informative relation embeddings can be
obtained. We empirically show that our proposed representations are useful for
solving hard analogy questions.