About iiWAS

Theme

From Data to Insight: Advancing Information Integration and Web Intelligence for Human-Centric Decision Making

Aims & Scope

iiWAS is a leading international conference for researchers, practitioners, and industry experts in the field of information integration and web intelligence. The conference serves as a forum for presenting and discussing the latest advancements, trends, challenges, and opportunities related to these areas.

iiWAS covers a broad range of topics, including data integration, knowledge representation and reasoning, web mining, social network analysis, natural language processing, machine learning, big data analytics, and more. The conference attracts attendees from diverse backgrounds, including computer science, information systems, data science, artificial intelligence, and other related fields.

The conference features keynote speeches by leading researchers and practitioners, paper presentations, workshops, and panel discussions. Attendees have the opportunity to learn about the latest research findings, exchange ideas, network with peers, and collaborate on new projects. They can also interact with industry experts showcasing the latest technologies, tools, and services related to information integration and web intelligence.

iiWAS provides a platform for sharing knowledge, promoting innovation, and advancing the state of the art in information integration and web intelligence. Through this conference, attendees can gain valuable insights and contribute to the advancement of the field, ultimately leading to the development of more intelligent and effective web applications and services.

Topics of Interest

The conference program will include invited talks, peer reviewed technical program, demos, tutorials, panels, industrial presentations and exhibitions around but not limited to the following topics:

Semantic Web, Linked Data, and Neurosymbolic AI
  • Ontology learning and evolution
  • Reasoning over linked data at web scale
  • Integration of symbolic reasoning with neural models
Knowledge Graphs, Graph Foundation Models, and GraphRAG
  • Large-scale knowledge graph construction and alignment
  • Graph neural networks and foundation models for graphs
  • Retrieval-augmented generation over knowledge graphs (GraphRAG)
Generative AI, Foundation Models, and LLMs
  • Efficient training and fine-tuning of foundation models
  • Domain adaptation and instruction tuning
  • Alignment, safety, and hallucination mitigation
Agentic AI, Autonomous Systems, and Multi-Agent Collaboration
  • Planning and reasoning in autonomous agents
  • Multi-agent coordination and negotiation
  • Tool use, API calling, and environment interaction
Explainable, Trustworthy, and Responsible AI
  • Explainability methods for deep and generative models
  • Bias detection and fairness in AI systems
  • Trust calibration and accountability frameworks
Human-AI Collaboration and Augmented Intelligence
  • Interactive decision-support systems
  • Adaptive human-in-the-loop learning
  • Cognitive augmentation in knowledge work
Multimodal AI and Cross-Modal Information Integration
  • Vision-language-audio unified representations
  • Cross-modal retrieval and fusion techniques
  • Multimodal reasoning and grounding
Retrieval-Augmented Generation (RAG) and Intelligent Knowledge Retrieval
  • Hybrid search (dense + symbolic retrieval)
  • Context optimization and memory systems for LLMs
  • Dynamic knowledge updating and grounding strategies
AI for Web Intelligence, Search, and Personalized Systems
  • Semantic search and intent understanding
  • Personalization via user modeling and context awareness
  • Adaptive ranking and recommendation systems
Federated Learning, Privacy-Preserving AI, and Data Governance
  • Secure aggregation and distributed learning
  • Differential privacy and anonymization techniques
  • Data sovereignty and governance frameworks
AI Security, Adversarial Learning, and Cyber-Resilient Systems
  • Adversarial attacks on models and defenses
  • Robust AI under distribution shifts
  • Secure model deployment and monitoring
Web Services, APIs, MCP Protocols, and Service Orchestration
  • Intelligent API discovery and composition
  • Model Context Protocol (MCP)-based orchestration
  • Semantic interoperability of web services
Data Spaces, Interoperability, and Cross-Platform Integration
  • Standardized data exchange frameworks
  • Federated and distributed data ecosystems
  • Semantic interoperability across domains
Edge AI, IoT Intelligence, and 6G Semantic Networks
  • On-device inference and model compression
  • IoT data fusion and streaming analytics
  • Semantic communication for 6G networks
Big Data Analytics, Real-Time Intelligence, and Streaming Systems
  • Scalable stream processing architectures
  • Real-time anomaly detection and analytics
  • High-velocity data integration pipelines
Blockchain, Decentralized AI, and Trusted Data Ecosystems
  • Decentralized model training and inference
  • Smart contracts for data sharing and governance
  • Verifiable AI and provenance tracking
Digital Twins, Smart Environments, and Intelligent Infrastructure
  • Real-time simulation and synchronization
  • AI-driven predictive maintenance systems
  • Urban-scale digital twin platforms
Social Media Intelligence, Misinformation, and AI for Society
  • Fake news detection and content verification
  • Sentiment and trend analysis at scale
  • Ethical content moderation systems
Open Knowledge Ecosystems, Collaborative Intelligence, and Open Science
  • Open data integration platforms
  • Collaborative AI research infrastructures
  • Reproducibility and shared benchmarks
AI for Smart Cities, Healthcare, Education, Industry, and Sustainability
  • Domain-specific intelligent decision systems
  • Resource optimization and sustainability analytics
  • AI-driven personalized services
Quantum AI, Emerging Computing Paradigms, and Hybrid Intelligence
  • Quantum machine learning algorithms
  • Hybrid classical-quantum systems
  • Novel computing architectures for AI
Ethical, Legal, Regulatory, and Societal Implications of AI
  • AI regulation and compliance frameworks
  • Legal responsibility and liability in AI systems
  • Societal impact assessment and governance
Future Directions in Web Intelligence and Agentic Web
  • Self-evolving web ecosystems
  • Autonomous knowledge ecosystems
  • Fully agent-driven web architectures