We propose a new class of methods for learning vector space embeddings of entities. While most existing methods focus on modelling similarity, our primary aim is to learn embeddings that are interpretable, in the sense that query terms have a direct geometric representation in the vector space. Intuitively, we want all entities that have some property (i.e. for which a given term is relevant) to be located in some well-de ned region of the space. is is achieved by imposing max-margin constraints that are derived from a bagof-words representation of the entities. e resulting vector spaces provide us with a natural vehicle for identifying entities that have a given property (or ranking them according to how much they have the property), and conversely, to describe what a given set of entities have in common. As we show in our experiments, our models lead to a substantially be er performance in a range of entity-oriented search tasks, such as list completion and entity ranking.
Methods focussing on building interpretable representations include MEm-bER REF which learns entity embeddings using max-margin constraints to encode the desideratum that (salient) properties of entities should have a simple geometric representation in the entity embedding.
MEmbER: Max-Margin Based Embeddings for Entity Retrieval
SIGIR '17
Computer Science