May 11, 2020 · We propose a novel end-to-end graph neural entity disambiguation model which fully exploits the global semantic information.

May 11, 2020 · We propose a novel end-to-end graph neural entity disambiguation model which fully exploits the global semantic information.

In this work, we propose a method that uses graph embeddings for integrating structured information from the knowledge base with unstructured information from ...

Sep 25, 2024 · Named Entity Disambiguation (NED) is a critical NLP task that involves resolving ambiguities in entity mentions by linking them to the correct entries in a ...

In order to make information from a semantic graph available for an entity linking system, we make use of graph embeddings. We use DeepWalk. (Perozzi et al., ...

Apr 3, 2021 · In this paper, we introduce ED-GNN based on three representative GNNs (GraphSAGE, R-GCN, and MAGNN) for medical entity disambiguation. We ...

Entity Disambiguation is the task of linking mentions of ambiguous entities to their referent entities in a knowledge base such as Wikipedia.

In this paper, to address the issue, we propose a novel end-to-end graph neural entity disambiguation model which fully exploits the global semantic information ...

Aug 4, 2022 · Understanding the role of knowledge graphs for the named entity disambiguation task in the healthcare domain.

Jul 10, 2019 · Compute accurate, real-time predictions from a Named Entity Disambiguation algorithm using graph embeddings.