Deep Learning -- TeaE: an Efficient Method for Improving the Precision of Teaching Evaluation -- Graph Fusion Multimodal Named Entity Recognition Based on Auxiliary Relation Enhancement -- Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension -- Multi-grained Logical Graph Network for Reasoning-based Machine Reading Comprehension -- Adaptive Prototype Learning with Common and Discriminative Features for Few-shot Relation Extraction -- Fine-grained Knowledge Enhancement for Empathetic Dialogue Generation -- Implicit Sentiment Extraction using Structure Generation with Sentiment Instructor Prompt Template -- SE-Prompt: Exploring Semantic Enhancement with Prompt Tuning for Relation Extraction -- Self-supervised Multi-view Clustering Framework with Graph Filtering and Contrast Fusion -- Semantic Selection and Multi-view Alignment for Image-Text Retrieval -- Voice Conversion with Denoising Diffusion Probabilistic GAN Models -- Symbolic & Acoustic: Multi-domain Music Emotion Modeling for Instrumental Music -- Document-level Relation Extraction with Relational Reasoning and Heterogeneous Graph Neural Networks -- A Chinese Named Entity Recognition Method based on Textual Information Perception Fusion -- Aspect-Based Sentiment Analysis via BERT and Multi-Scale CBAM -- A novel adaptive distribution distance-based feature selection method for video traffic identification -- SVIM: a Skeleton-based View-invariant Method for Online Gesture Recognition -- A Unified Information Diffusion Prediction Model based on Multi-task Learning -- Learning Knowledge Representation with Entity Concept Information -- Domain Adaptive Pre-trained Model for Mushroom Image Classification -- Training Noise Robust Deep Neural Networks with Self-supervised Learning -- Path integration enhanced graph attention network -- Graph Contrastive Learning with HybridNoise Augmentation for Recommendation -- User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation -- Multi-Self-Supervised Light Graph Convolution Network for Social Recommendation -- A Poisoning Attack Based on Variant Generative Adversarial Networks in Recommender Systems -- Label Correlation guided Feature Selection for Multi-label Learning -- Iterative Encode-and-Decode Graph Neural Network -- Community Detection in Temporal Biological Metabolic Networks based on Semi-NMF Method with Node Similarity Fusion -- UKGAT: Uncertain Knowledge Graph Embedding Enriched KGAT for Recommendation -- Knowledge Graph Link Prediction Model Based on Attention Graph Convolutional Network -- Knowledge Graph Embedding with Relation Rotation and Entity Adjustment by Quaternions -- Towards time-variant-aware Link Prediction in Dynamic Graph through Self-supervised Learning -- Adaptive Heterogeneous graph Contrastive clustering with Multi-Similarity -- Multi-Teacher Local Semantic Distillation from Graph Neural Networks -- AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining -- Rethinking the Evaluation of Deep Neural Network Robustness -- A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training -- TSCMR:Two-Stage Cross-Modal Retrieval -- Effi-Emp: An AI based approach towards positive empathic expressions -- Industry Track Papers -- Research on Image Segmentation Algorithm Based on Level Set. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute) -- Predicting learners' performance using MOOC clickstream -- A Fine-grained Verification Method for Blockchain Data Based on Merkle Path Sharding -- A Privacy Preserving Method for Trajectory Data Publishing Based on Geo-indistinguishability -- HA-CMNet: A Driver CTR Model for Vehicle-Cargo Matching in O2O Platform -- A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Capability Evaluation of Manufacturing Enterprises -- A method for data exchange and management in the military industry field. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute) -- Multi-region Quality Assessment based on Spatial-Temporal Community Detection from Computed Tomography Images.

This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21-23, 2023. The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.