UID:
almahu_9949335243702882
Umfang:
XXVIII, 753 p. 256 illus., 168 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2022.
ISBN:
9783031109836
Serie:
Lecture Notes in Artificial Intelligence ; 13368
Inhalt:
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).
Anmerkung:
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 Parking Occupancy 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.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783031109829
Weitere Ausg.:
Printed edition: ISBN 9783031109843
Sprache:
Englisch
DOI:
10.1007/978-3-031-10983-6
URL:
https://doi.org/10.1007/978-3-031-10983-6
