Knowledge Graphs and Semantic Computing

.- Knowledge Graph Construction and Integration.

.- A Cross-Subgraph Attention Fusion and Comparison Method for Contrastive Learning Based Knowledge Graph Completion.

.- A Preliminary Attempt to Generate a Sichuan Dialect Handbook by LLMs.

.- EDREL: Document-level Relation Extraction with Evidence and Logical Rules.

.- Knowledge Retrieval-Augmented Interest learning for Recommendation.

.- Large Models Enhanced by Knowledge Graphs.

.- Multi-granularity Hierarchical RAG for Welding Parameter Recommendation.

.- Lag-Relative Sparse Attention In Long Context Training.

.- VIKA: Vectorized Indispensable Knowledge-subgraph Augmentation for Large Language Models.

.- Traff-LLM: A Spatio-Temporal Knowledge-Guided Large Language Model for Traffic Flow Prediction.

.- Applications of Knowledge Graphs and Large Models/Agents.

.- MAEPS: Multi-Agent Event Prediction System Based on Human Expert Team Collaboration Simulation.

.- Zero-shot Instruction Generation via Dual-Alignment Instruction Wrappers with Summary-Text fused instruction wrappers.

.- FEFT: A Feedback-enhanced Evaluation Fine-tuning Framework for Financial Report Summarization.

.- Iterative Generation Method for Factual QA in Large Language Models Based on Semantic Entropy Verification.

.- Open Resources for Knowledge Graphs and Large Models.

.- Autism Children Education Knowledge Graph: Construction and Validation.

.- C-Voice: Culturally-grounded Multi-dimensional Alignment of LLMs with Chinese Social Values.

.- Evaluations.

.- HiParse-RAG: A High-Fidelity Document Parsing and Hybrid Retrieval Multi-Model Fusion Framework for Complex Academic Question Answering.

.- HybriDoc: An Adaptive Multi-Path Framework for End-to-End Document Structure Extraction.

.- Pre-training for Document Structure Extraction with Lightweight Model Architecture.

.- Robust Detection of AI-Generated Text: Insights on Evolving LLMs and Adversarial Data.

.- A Fact-Aware Cascaded Framework for Dynamic-granularity Timeline Summarization.

.- Multi-Agent for Dynamic-Granularity Timeline Summarization.

.- Advancing Grounded Multimodal NER via Self-Reflective Prompt Refinement and Visual Noise Mitigation.

.- ReFineG: Synergizing Small Supervised Models and LLMs for Low-Resource Grounded Multimodal NER.

• Erscheinungsdatum: 01.07.2026
• Autor/Autorin: Jiye Liang
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 39.5 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 288 Seiten
• ISBN: 9789819585250
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