The increasing complexity of modern digital ecosystems calls for intelligent, adaptive, and semantically enriched architectures. This workshop explores how Artificial Intelligence (AI) can effectively combine semantic technologies to create, manage, and reason over knowledge ecosystems. It aims to gather researchers, practitioners, and domain experts interested in converging knowledge representation, AI reasoning, and semantic interoperability across domains such as cloud-native systems, IoT, etc.
The workshop will cover a range of topics related to the use of AI for Semantic Based Knowledge Ecosystems (but not limited to):
- Semantic-based knowledge representation and formal ontologies
- AI reasoning over ontologies and knowledge graphs
- Ontology-based representation in networks and IoT environments
- Semantic anomaly detection and explainable AI
- Hybrid AI (symbolic + statistical learning)
- Semantic trust and data provenance modelling
- Semantic-AI frameworks and toolkits
- Multi-modal and cross-modal knowledge representation and reasoning
- Ontology alignment, matching and automated ontology construction using AI and LLMs
- Semantic-aware Large Language Models (LLMs)
- Knowledge graph construction, enrichment and completion using AI and LLMs
- Ethics, fairness, and bias detection in semantic AI systems
- Green AI for sustainable knowledge ecosystems
The workshop welcomes papers on AI’s applicability within semantic-based Knowledge Ecosystems. Real-world case studies and applied research contributions demonstrating semantic AI systems’ practical deployment and impact in domains such as healthcare, manufacturing, smart cities, and digital governance are highly encouraged.
Two types of papers can be submitted:
- Long papers will be 10 pages of content and unlimited references in the MEDES 2025 submission style. They should report on novel, unpublished work.
- Extended abstracts will be 2 pages in the MEDES 2025 submission style and summarise recent and novel unpublished work fitting the workshop topics.
Each paper will be reviewed by at least two members of the Program Committee based on technical quality, relevance, significance, and clarity.
