SEMANTIC COMPUTING AND AI

This collection reflects key milestones in research, addressing fundamental theories, emerging trends, and real-world applications of two closely connected disciplines of computer science: semantic computing and artificial intelligence (AI).

The rapid evolution of computing has led to transformative advancements in semantic computing and AI simultaneously. Both fields have driven innovation in data processing, knowledge representation, and intelligent decision-making. This book presents a carefully curated selection of articles from the International Journal of Semantic Computing (IJSC), spanning from 2007 to 2025, capturing the journey in which the two disciplines became more and more connected, while expanding our understanding of machine intelligence.

The intersection of semantic computing and AI has become increasingly significant, offering sophisticated solutions to complex computational problems and enhancing the way machines interpret and interact with human knowledge. This book serves as a valuable resource for researchers, practitioners, and students seeking to understand the evolution and impact of semantic computing and AI.

Contents:

• Semantic Computing & AI - Fundamentals, Challenges and Solutions:
  • LLMs: Their Past, Promise, and Problems (G F Luger)
  • Problems, Solutions, and Semantic Computing (P C-Y Sheu and C V Ramamoorthy)
• Machine Learning as the Foundation of AI:
  • Random Walk Term Weighting for Improved Text Classification (S Hassan, R Mihalcea and C Banea)
  • PPeer-to-Peer Reasoning for Interlinked Ontologies (A Schlicht and H Stuckenschmidt)
  • Optimal Sequential Grouping for Robust Video Scene Detection Using Multiple Modalities (D Rotman, D Porat and G Ashour)
  • Knowledge Graph-Based Explainable Artificial Intelligence for Business Process Analysis (A Füβl, V Nissen and S H Heringklee)
• Deep Learning as a Specialized Discipline:
  • Deep Learning (X Hao, G Zhang and S Ma)
  • Early and Late Level Fusion of Deep Convolutional Neural Networks for Visual Concept Recognition (H Ergun, Y C Akyuz, M Sert and J Liu)
  • Evolving Deep Neural Networks with Cultural Algorithms for Real-Time Industrial Applications (F Waris, R G Reynolds and J Lee)
• Practical Applications of ML, DL, and AI:
  • Multimodal Information Fusion of Audio Emotion Recognition Based on Kernel Entropy Component Analysis (Z Xie and L Guan)
  • Automatic Video Event Detection for Imbalance Data Using Enhanced Ensemble Deep Learning (S Pouyanfar and S-C Chen)
  • Knowledge Extraction of Adaptive Structural Learning of Deep Belief Network for Medical Examination Data (S Kamada, T Ichimura and T Harada)
  • Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook (E Kurshan and H Shen)
  • Automatic Title Generation for Learning Resources and Pathways with Pre-trained Transformer Models (P Mishra, C Diwan, S Srinivasa and G Srinivasaraghavan)

Readership: Advanced undergraduate and graduate students, researchers and practitioners in the fields of semantic computing, artificial intelligence, machine learning, and general computer science.