Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence [electronic resource] : 8th China Conference, CCKS 2023, Shenyang, China, August 24-27, 2023, Revised Selected Papers / edited by Haofen Wang, Xianpei Han, Ming Liu, Gong Cheng, Yongbin Liu, Ningyu Zhang.

Colaborador(es): Wang, Haofen [editor.] | Han, Xianpei [editor.] | Liu, Ming [editor.] | Cheng, Gong [editor.] | Liu, Yongbin [editor.] | Zhang, Ningyu [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Communications in Computer and Information Science ; 1923Editor: Singapore : Springer Nature Singapore : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: XIX, 364 p. 93 illus., 79 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819972241Tema(s): Artificial intelligence | Application software | Information storage and retrieval systems | Database management | Data mining | Information technology -- Management | Artificial Intelligence | Computer and Information Systems Applications | Information Storage and Retrieval | Database Management | Data Mining and Knowledge Discovery | Computer Application in Administrative Data ProcessingFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q334-342TA347.A78Recursos en línea: Libro electrónicoTexto
Contenidos:
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 -- Moral Essential 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.
En: Springer Nature eBookResumen: 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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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 -- Moral Essential 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.

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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