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Entity Embeddings

Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.

Papers

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Showing 81–90 of 151 papers

TitleStatusHype
SocialVec: Social Entity EmbeddingsCode0
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework—0
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification—0
Principled Representation Learning for Entity Alignment—0
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity EmbeddingsCode0
Enhancing Natural Language Representation with Large-Scale Out-of-Domain CommonsenseCode0
GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale—0
Global entity alignment with Gated Latent Space Neighborhood Aggregation—0
Improving Entity Linking by Encoding Type Information into Entity Embeddings—0
Graph Neural Pre-training for Enhancing Recommendations using Side Information—0
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