1. School of Computing
FACULTY OF ENGINEERING
Combining Ontologies and Machine Learning to Capture Tacit Knowledge in Complex Decision Making
Yiannis Gatsoulis, Owais Mehmood, Vania Dimitrova, Tony Cohn
Artificial Leeds 1

2. Diagnosing Tunnels
Maintenance operations and the impact of a rail tunnel malfunction can be costly and catastrophic
high costs
disruptions
economic impact
wikipedia
Travel-and-Tour-world
BBC
Tunnel age [>100 years]
External factors [traffic, weather]

3. Complex process, few experts, tacit knowledge
Tunnel Diagnosis Process
Complex process, few experts, tacit knowledge
Tunnel Inspections
Pathology Finding
Potential Degradation
Observed disorders
Influencing factors
Contextual Data
geology
history
traffic
Actions:
Survey, additional investigations
Repairs
- Urgency (schedule)
PADTUN goal:
Decision support for
pathology assessment and diagnosis of tunnels

4. Capturing Tacit Knowledge
Highly complex decision making in domains with high impact
Expert knowledge rare and expensive
Long years of experience acting with intuition
Conventional ontological modeling
Work with experts and capture the knowledge

5. Pathologies and Disorders

6. Pathology Identification

7. Ontological Modelling is not Enough
Ontological model may not capture the true complexity of the decision process
Validation is important and laborious
Inaccurate or missing rules
Takes very long to identify these
Some rules are ‘more reliable’ than others (experts cannot articulate this)
Extension – add crucial aspects of the decision process
Identify rules that cannot be articulated by the experts

8. Combining Ontologies and Machine Learning
We have built an ontology for assessing tunnel pathologies
Applied on data providing “conventional” fitness validation
Time consuming task
Closed-loop design process
We have survey data, contextual data and decisions for tunnels
Support maintenance planning – identify risk levels.
- use the ontology to enrich the feature space of ML models
Validate and extend the pathology assessment rules in the ontology
- treat ‘pathology identification’ as classification or regression task

9. Lessons Learnt Tacit Knowledge is hard to articulate but we can:
Work with experts
Develop bootstrapped ontological models
Adopt a generic approach (e.g. from social science or cognitive science)
When data about human behaviour is available we can:
Validate ontological models with data analytics
Extend the models – capture the aspects that cannot be articulated
Use different sources, e.g. data bases or social web