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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 61–70 of 151 papers

TitleStatusHype
GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale—0
Global entity alignment with Gated Latent Space Neighborhood Aggregation—0
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings—0
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph—0
JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase—0
Knowledge Representation with Conceptual Spaces—0
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion—0
DisenE: Disentangling Knowledge Graph Embeddings—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
Improving Question Answering over Knowledge Graphs Using Graph Summarization—0
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