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Entity linking methods based on dense retrieval are an efficient and widelyused solution in large-scale applications, but they fall short of theperformance of generative models, as they are sensitive to the structure of theembedding space. In order to address this issue, this paper introduces DUCK, anapproach to infusing structural information in the space of entityrepresentations, using prior knowledge of entity types. Inspired by duck typingin programming languages, we propose to define the type of an entity based onthe relations that it has with other entities in a knowledge graph. Then,porting the concept of box embeddings to spherical polar coordinates, wepropose to represent relations as boxes on the hypersphere. We optimize themodel to cluster entities of similar type by placing them inside the boxescorresponding to their relations. Our experiments show that our method sets newstate-of-the-art results on standard entity-disambiguation benchmarks, itimproves the performance of the model by up to 7.9 F1 points, outperforms othertype-aware approaches, and matches the results of generative models with 18times more parameters.

Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings