Improving Entity Disambiguation by Reasoning over a Knowledge Base
- URL: http://arxiv.org/abs/2207.04106v1
- Date: Fri, 8 Jul 2022 19:13:53 GMT
- Title: Improving Entity Disambiguation by Reasoning over a Knowledge Base
- Authors: Tom Ayoola, Joseph Fisher, Andrea Pierleoni
- Abstract summary: We introduce an ED model which links entities by reasoning over a symbolic knowledge base.
Our model surpasses state-of-the-art baselines on six well-established ED datasets by 1.3 F1 on average.
- Score: 2.223733768286313
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent work in entity disambiguation (ED) has typically neglected structured
knowledge base (KB) facts, and instead relied on a limited subset of KB
information, such as entity descriptions or types. This limits the range of
contexts in which entities can be disambiguated. To allow the use of all KB
facts, as well as descriptions and types, we introduce an ED model which links
entities by reasoning over a symbolic knowledge base in a fully differentiable
fashion. Our model surpasses state-of-the-art baselines on six well-established
ED datasets by 1.3 F1 on average. By allowing access to all KB information, our
model is less reliant on popularity-based entity priors, and improves
performance on the challenging ShadowLink dataset (which emphasises infrequent
and ambiguous entities) by 12.7 F1.
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