This volume set constitutes the refereed proceedings of the 16th International Conference on Knowledge Science, Engineering and Management, KSEM 2023, which was held in Guangzhou, China, during August 16–18, 2023.
The 114 full papers and 30 short papers included in this book were carefully reviewed and selected from 395 submissions. They were organized in topical sections as follows: knowledge science with learning and AI; knowledge engineering research and applications; knowledge management systems; and emerging technologies for knowledge science, engineering and management.
Knowledge Science with Learning and AI.- Joint Feature Selection and
Classifier Parameter Optimization: A Bio-inspired Approach.- Automatic
Gaussian Bandwidth Selection for Kernel Principal Component
Analysis.- Boosting LightWeight Depth Estimation Via Knowledge
Distillation.- Graph Neural Network with Neighborhood Reconnection.- Critical
Node Privacy Protection Based on Random Pruning of Critical
Trees.- DSEAformer: Forecasting by De-stationary Autocorrelation with
Edgebound.- Multitask-based Cluster Transmission for Few-Shot Text
Classification.- Hyperplane Knowledge Graph Embedding with Path Neighborhoods
and Mapping Properties.- RTAD-TP: Real- Time Anomaly Detection Algorithm for
Univariate Time Series Data Based on Two- Parameter
Estimation.- Multi-Sampling Item Response Ranking Neural Cognitive Diagnosis
with Bilinear Feature Interaction.- A Sparse Matrix Optimization Method for
Graph Neural Networks Training.- Dual-dimensional Refinement of Knowledge
Graph Embedding Representation.- Contextual Information Augmented Few-Shot
Relation Extraction.- Dynamic and Static Feature-aware Microservices
Decomposition via Graph Neural Networks.- An Enhanced Fitness-distance
Balance Slime Mould Algorithm and Its Application in Feature Selection.- Low
Redundancy Learning for Unsupervised Multi-view Feature Selection.- Dynamic
Feed-Forward LSTM.- Black-box Adversarial Attack on Graph Neural Networks
Based on Node Domain Knowledge.- Role and Relationship-Aware Representation
Learning for Complex Coupled Dynamic Heterogeneous Networks.- Twin Graph
Attention Network with Evolution Pattern Learner for Few-Shot Temporal
Knowledge Graph Completion.- Subspace Clustering with Feature Grouping for
Categorical Data.- Learning Graph Neural Networks on Feature-Missing
Graphs.- Dealing with Over-reliance on Background Graph for Few-shot
Knowledge Graph Completion.- Kernel-based feature extraction for time series
clustering.- Cluster Robust Inference for embedding-based Knowledge Graph
Completion.- Community-enhanced Contrastive Siamese networks for Graph
Representation Learning.- Distant Supervision Relation Extraction with
Improved PCNN and Multi-level Attention.- Enhancing Adversarial Robustness
via Anomaly-aware Adversarial Training.- An Improved Cross-Validated
Adversarial Validation Method.- EACCNet: Enhanced Auto-Cross Correlation
Network for Few-Shot Classification.- Joint Label-Structure Estimation from
Multifaceted Graph Data.- Dual Channel Knowledge Graph Embedding with
Ontology Guided Data Augmentation.- Multi-Dimensional Graph Rule
Learner.- MixUNet: A Hybrid Retinal Vessels Segmentation Model Combining The
Latest CNN and MLPs.- Robust Few-shot Graph Anomaly Detection via Graph
Coarsening.- An Evaluation Metric for Prediction Stability with Imprecise
Data.- ReducingThe Teacher-Student Gap Via Elastic Student.
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