Entity Embeddings
70 papers with code • 0 benchmarks • 2 datasets
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.
Benchmarks
These leaderboards are used to track progress in Entity Embeddings
Latest papers with no code
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users
It is essential to provide recommendations that are both personalized and diverse, rather than solely relying on achieving high rank-based performance, such as Click-through Rate, Recall, etc.
Unlocking the Power of Large Language Models for Entity Alignment
To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy.
Embedding Knowledge Graphs in Degenerate Clifford Algebras
We propose to consider nilpotent base vectors with a nilpotency index of two.
Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment
The final prediction of the equivalent entity is derived from the LLM's output.
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction
Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM).
Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks
Embedding-based models usually need fine-tuning on new entity embeddings, and hence are difficult to be directly applied to inductive link prediction tasks.
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion
In this paper, we propose a novel dynamic convolutional embedding model ConvD for knowledge graph completion, which directly reshapes the relation embeddings into multiple internal convolution kernels to improve the external convolution kernels of the traditional convolutional embedding model.
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation
Medical dialogue generation relies on natural language generation techniques to enable online medical consultations.
Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models
Large Language Models (LLMs) are evolving to integrate multiple modalities, such as text, image, and audio into a unified linguistic space.
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models
However, they struggle with semantically rich real-world entities due to limited structural information and fail to generalize to unseen entities.