https://github.com/zhixiangsu/krst
공식 코드Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer
RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces
Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
Start from Zero: Triple Set Prediction for Automatic Knowledge Graph Completion
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Making Large Language Models Perform Better in Knowledge Graph Completion
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
Relational Message Passing for Fully Inductive Knowledge Graph Completion
Noise-powered Multi-modal Knowledge Graph Representation Framework
TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.