https://github.com/csjywu1/adkgd
공식 코드ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training
medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs
Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion
FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion
Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion
Joint Matrix-Tensor Factorization for Knowledge Base Inference
Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
Traceable LLM-based validation of statements in knowledge graphs
PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model
Contextualization Distillation from Large Language Model for Knowledge Graph Completion
AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning
Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph
Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
Differentiating Concepts and Instances for Knowledge Graph Embedding
Knowledge Graph Completion with Relation-Aware Anchor Enhancement
Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphs
PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs
Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
A Dual-Perspective Metaphor Detection Framework Using Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.