https://github.com/zjukg/lpkg
공식 코드Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
Papers with Code 아카이브
Knowledge Graphs
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- 평가표
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zjukg/maco
공식 코드https://github.com/zjukg/maker
공식 코드Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
https://github.com/zjukg/momok
공식 코드https://github.com/zjukg/morse
공식 코드Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
https://github.com/zjukg/mygo
공식 코드Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
https://github.com/zjukg/neuralkg
공식 코드NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
https://github.com/zjukg/trustuqa
공식 코드TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
https://github.com/zjunlp/Generative_KG_Construction_Papers
공식 코드https://github.com/zjunlp/MKG_Analogy
공식 코드https://github.com/zjunlp/MKGformer
공식 코드Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
https://github.com/zjunlp/autokg
공식 코드LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
https://github.com/zjunlp/oneedit
공식 코드OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
https://github.com/zjunlp/ontoprotein
공식 코드OntoProtein: Protein Pretraining With Gene Ontology Embedding
https://github.com/zjunlp/promptkg
공식 코드LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
https://github.com/zjunlp/relphormer
공식 코드Relphormer: Relational Graph Transformer for Knowledge Graph Representations
https://github.com/zukangy/kgp-curiousllm
공식 코드CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge Graph Prompting
https://github.com/zwt233/Grain
공식 코드Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization
https://github.com/zxd-octopus/vricr
공식 코드Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation
https://github.com/zychen423/KE-VIST
공식 코드https://gitlab.com/boshko.koloski/codename_fn_b
공식 코드Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles
https://gitlab.com/patryk.preisner/mkga
공식 코드Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
https://gitlab.com/wxwilcke/mrgcn
공식 코드End-to-End Entity Classification on Multimodal Knowledge Graphs
https://us-south.git.cloud.ibm.com/anu-kg/legal-kg
공식 코드https://worksheets.codalab.org/worksheets/0xf215deb05edf44a2ac353c711f52a25f
공식 코드QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://worksheets.codalab.org/worksheets/0xfcace41fdeec45f3bc6ddf31107b829f
공식 코드https://zenodo.org/record/7916716
공식 코드Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text
https://github.com/01-ai/yi
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
https://github.com/AKSW/NLIWOD
https://github.com/AutoML-4Paradigm/S2S
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
https://github.com/AutoML-Research/KGBench
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
https://github.com/AutoML-Research/S2S
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
https://github.com/Blidge/KGReasoning
https://github.com/BunsenFeng/news_stance_detection
KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
https://github.com/CMSC470-Team/Model
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/CogComp/JEANS
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.