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Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence de Haofen Wang

Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence

Editat de Haofen Wang, Xianpei Han, Ming Liu, Gong Cheng, Yongbin Liu, Ningyu Zhang
en Limba Engleză Paperback – 28 oct 2023
This book constitutes the refereed proceedings of the 8th China Conference on Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence, CCKS 2023, held in Shenyang, China, during August 24–27, 2023. 

The 28 full papers included in this book were carefully reviewed and selected from 106 submissions. They were organized in topical sections as follows: ​knowledge representation and knowledge graph reasoning; knowledge acquisition and knowledge base construction; knowledge integration and knowledge graph management; natural language understanding and semantic computing; knowledge graph applications; knowledge graph open resources; and evaluations.
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Specificații

ISBN-13: 9789819972234
ISBN-10: 981997223X
Pagini: 384
Ilustrații: XIX, 364 p. 93 illus., 79 illus. in color.
Dimensiuni: 155 x 235 x 21 mm
Greutate: 0.58 kg
Ediția:1st edition 2023
Editura: Springer
Locul publicării:Singapore, Singapore

Cuprins

​Knowledge Representation and Knowledge Graph Reasoning.- Dynamic Weighted Neural Bellman-Ford Network for Knowledge Graph Reasoning.- CausE: Towards Causal Knowledge Graph Embedding.- Exploring the Logical Expressiveness of Graph Neural Networks by establishing a connection with C2.- Research on Joint Representation Learning Methods for Entity Neighborhood Information and Description Information.- Knowledge Acquisition and Knowledge Base Construction.- Harvesting Event Schemas from Large Language Models.- NTDA: Noise-Tolerant Data Augmentation for Document-Level Event Argument Extraction.- Event-Centric Opinion Mining via In-Context Learning with ChatGPT.- Relation repository based adaptive clustering for Open Relation Extraction.- Knowledge Integration and Knowledge Graph Management.- LNFGP: Local Node Fusion-based Graph Partition By Greedy Clustering.- Natural Language Understanding and Semantic Computing.- Multi-Perspective Frame Element Representation for Machine Reading Comprehension.- A Generalized Strategy of Chinese Grammatical Error Diagnosis based on Task Decomposition and Transformation.- Conversational Search based on Utterance-Mask-Passage Post-training.- Knowledge Graph Applications.- Financial Fraud Detection based on Deep Learning: towards Large-scale Pre-Training Transformer Models.- GERNS: A Graph Embedding with Repeat-free Neighborhood Structure for Subgraph Matching Optimization.- Feature Enhanced Structured Reasoning for Question Answering.- Knowledge Graph Open Resources.- Conditional Knowledge Graph: Design, Dataset and a Preliminary Model.- ODKG: An Official Document Knowledge Graph for the Effective Management.- CCD-ASQP: A Chinese Cross-domain Aspect Sentiment Quadruple Prediction Dataset.- CCD-ASQP: A Chinese Cross-domain Aspect Sentiment Quadruple Prediction Dataset.- MoralEssential Elements: MEE - A Dataset for Moral Judgement.- Evaluations.- Improving Adaptive Knowledge Graph Construction via Large Language Models with Multiple Views.- Single Source Path-based Graph Neural Network for Inductive Knowledge Graph Reasoning.- A Graph Learning Based Method for Inductive Knowledge Graph Relation Prediction.- LLM-Based Sparql Generation with selected Schema from Large scale Knowledge Base.- Robust NL-to-Cypher Translation for KBQA: Harnessing Large Language Model with Chain of Prompts.- In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models.- A Military Domain Knowledge-based Question Answering Method Based on Large Language Model Enhancement.- Advanced PromptCBLUE Performance: A Novel Approach Leveraging Large Language Models.