https://github.com/violet24k/sphere
공식 코드SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval
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.
SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval
Neural Compositional Rule Learning for Knowledge Graph Reasoning
KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG
Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
Do Language Models Perform Generalizable Commonsense Inference?
Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs
CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
Structural Entropy Guided Agent for Detecting and Repairing Knowledge Deficiencies in LLMs
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment
Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.