Neural variational entity set expansion for automatically populated knowledge graphs
Information Retrieval Journal, 2019•Springer
We propose Neural variational set expansion to extract actionable information from a noisy
knowledge graph (KG) and propose a general approach for increasing the interpretability of
recommendation systems. We demonstrate the usefulness of applying a variational
autoencoder to the Entity set expansion task based on a realistic automatically generated
KG.
knowledge graph (KG) and propose a general approach for increasing the interpretability of
recommendation systems. We demonstrate the usefulness of applying a variational
autoencoder to the Entity set expansion task based on a realistic automatically generated
KG.
Abstract
We propose Neural variational set expansion to extract actionable information from a noisy knowledge graph (KG) and propose a general approach for increasing the interpretability of recommendation systems. We demonstrate the usefulness of applying a variational autoencoder to the Entity set expansion task based on a realistic automatically generated KG.
Springer
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