Semi-Automated Knowledge Engineering and Process Mapping for Total Airport Management
📰 ArXiv cs.AI
Semi-automated knowledge engineering and process mapping for Total Airport Management
Action Steps
- Identify and document airport operations using a standardized terminology
- Develop a machine-readable knowledge graph to integrate fragmented data sources
- Apply semantic mapping to resolve inconsistencies and improve data quality
- Implement a process mapping framework to support Total Airport Management initiatives
Who Needs to Know This
Airport management teams and IT stakeholders can benefit from this framework to improve data consistency and communication across multiple stakeholders. This can also be useful for software engineers and data scientists working on airport management systems
Key Insight
💡 A domain-grounded, machine-readable knowledge graph can help resolve data silos and semantic inconsistencies in airport operations
Share This
🛫️ Improving airport management with semi-automated knowledge engineering and process mapping! 💡
Key Takeaways
Semi-automated knowledge engineering and process mapping for Total Airport Management
Full Article
Title: Semi-Automated Knowledge Engineering and Process Mapping for Total Airport Management
Abstract:
arXiv:2603.26076v1 Announce Type: new Abstract: Documentation of airport operations is inherently complex due to extensive technical terminology, rigorous regulations, proprietary regional information, and fragmented communication across multiple stakeholders. The resulting data silos and semantic inconsistencies present a significant impediment to the Total Airport Management (TAM) initiative. This paper presents a methodological framework for constructing a domain-grounded, machine-readable Kn
Abstract:
arXiv:2603.26076v1 Announce Type: new Abstract: Documentation of airport operations is inherently complex due to extensive technical terminology, rigorous regulations, proprietary regional information, and fragmented communication across multiple stakeholders. The resulting data silos and semantic inconsistencies present a significant impediment to the Total Airport Management (TAM) initiative. This paper presents a methodological framework for constructing a domain-grounded, machine-readable Kn
DeepCamp AI