Knowledge Science, Engineering and Management (eBook, PDF)

15th International Conference, KSEM 2022, Singapore, August 6-8, 2022, Proceedings, Part I
Redaktion: Memmi, Gerard; Qiu, Meikang; Zhang, Tianwei; Kong, Linghe; Yang, Baijian

• Format: PDF

Produktbeschreibung

The three-volume sets constitute the refereed proceedings of the 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022, held in Singapore, during August 6-8, 2022.

The 169 full papers presented in these proceedings were carefully reviewed and selected from 498 submissions. The papers are organized in the following topical sections:

Volume I:

Knowledge Science with Learning and AI (KSLA)

Volume II:

Knowledge Engineering Research and Applications (KERA)

Volume III:

Knowledge Management with Optimization and Security (KMOS)

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 753
• Erscheinungstermin: 19. Juli 2022
• Englisch
• ISBN-13: 9783031109836
• Artikelnr.: 64378021

Inhaltsangabe

Knowledge Science with Learning and AI (KSLA).- A decoupled YOLOv5 with deformable convolution and multi-scale attention.- OTE: An Optimized Chinese Short Text Matching Algorithm based on External Knowledge.- KIR: A Knowledge-enhanced Interpretable Recommendation Method.- ICKEM: a tool for estimating one's understanding of conceptual knowledge.- Cross-perspective Graph Contrastive Learning.- A Multi-scale Convolution and Gated Recurrent Unit Based Network for Limit Order Book Prediction.- Pre-train Unified Knowledge Graph Embedding with Ontology.- Improving Dialogue Generation with Commonsense Knowledge Fusion and Selection.- A Study of Event Multi-triple Extraction Methods Based on Edge-Enhanced Graph Convolution Networks.- Construction Research and Applications of Industry Chain Knowledge Graphs.- Query and Neighbor-aware Reasoning based Multi-hop Question Answering over Knowledge Graph.- Question Answering over Knowledge Graphs with Query Path Generation.- Improving ParkingOccupancy Prediction in Poor Data Conditions through Customization and Learning to Learn.- Knowledge Concept Recommender Based on Structure Enhanced Interaction Graph Neural Network.- Answering Complex Questions on Knowledge Graphs.- Multi-Attention User Information Based Graph Convolutional Networks for Explainable Recommendation.- Edge-shared GraphSAGE: A New Method of Buffer Calculation for Parallel Management of Big Data Project Schedule.