knowledge graphs
Knowledge graphs are structured representations of information that capture relationships between entities in a graph format. In AI, they are used to enhance natural language understanding and reasoning capabilities by providing contextual knowledge and semantic organization of data.
- Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?
- Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness
- DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion
- GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
- KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
- KGGen: Extracting Knowledge Graphs from Plain Text with Language Models
- LBMKGC: Large Model-Driven Balanced Multimodal Knowledge Graph Completion
- NeuSymEA: Neuro-symbolic Entity Alignment via Variational Inference
- ReMindRAG: Low-Cost LLM-Guided Knowledge Graph Traversal for Efficient RAG
- Scalable Feature Learning on Huge Knowledge Graphs for Downstream Machine Learning
- Semantic-KG: Using Knowledge Graphs to Construct Benchmarks for Measuring Semantic Similarity
- Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
- UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models
- WALL-E: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents