Memmi / Yang / Kong Knowledge Science, Engineering and Management

15th International Conference, KSEM 2022, Singapore, August 6–8, 2022, Proceedings, Part I
Erscheinungsjahr 2022
ISBN: 978-3-031-10983-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 753 Seiten

Reihe: Lecture Notes in Computer Science

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)

Zielgruppe

Research

Autoren/Hrsg.

Memmi, Gerard

Yang, Baijian

Kong, Linghe

Zhang, Tianwei

Qiu, Meikang

Inhaltsverzeichnis

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.