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.

Latest papers with no code

Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users

no code yet • 27 Mar 2024

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

no code yet • 23 Feb 2024

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

no code yet • 6 Feb 2024

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

no code yet • 30 Jan 2024

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

no code yet • 18 Dec 2023

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

no code yet • 16 Dec 2023

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

no code yet • 11 Dec 2023

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

no code yet • 24 Nov 2023

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

no code yet • 27 Oct 2023

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

no code yet • 16 Aug 2023

However, they struggle with semantically rich real-world entities due to limited structural information and fail to generalize to unseen entities.