Research Topic
Summary
<Approaches>
Graph Representation Learning (그래프 표현학습)
-Graph Neural Networks (GNNs) for Node/Edge/Graph Embedding
-Knowledge Graph Representation/Completion/Validation/Construction
-Context-Aware Knowledge Graph Representation and Relational Learning
Large Language Models (대형 언어모델)
-Multi-Modal & Knowledge-Enhanced Foundation Models
-Knowledge & LLM Distillation for Efficient Model Development
-Advanced Prompt Engineering: Chain-of-Thought (CoT), and Retrieval-Augmented Generation (RAG)
Synergizing LLMs and Graphs (그래프·언어모델 통합)
-Text-to-Graph & Graph-to-Text Generation
-Graph-Structured Interaction for LLMs (GraphRAG, Graph-driven LLM Agents)
-Knowledge-grounded & Context-aware Response Generation with LLMs
<Applications>
Natural Language Processing (자연어처리)
-Question Answering, Information Retrieval & Extraction
-Document Analysis (Sentiment, Opinion, Topic, NER, Summarization)
Recommender Systems (추천시스템)
-Knowledge-enhanced & Explainable Recommendations
-Conversational & Graph-based Recommendations
Graph Analytics and Prediction (그래프 분석·예측)
-Node & Graph Classification Tasks
-Link Prediction and Knowledge Graph Completion
Poster
