# AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
## 4 Methodologies for Integrating Domain Knowledge into AI/ML
### Handling Dynamic Domains Where Knowledge Evolves over Time  
Handling dynamic domains where knowledge evolves is a significant challenge in AI/ML. Models must understand the current state of knowledge [121] and adapt to new information, trends, and patterns as they emerge [122], requiring a flexible, responsive approach to development and maintenance [123].  
Adaptive learning is essential for AI systems in dynamic domains like healthcare, where data and knowledge continuously evolve. Systems must learn from new data inputs to maintain predictive accuracy and relevance [110]. Techniques such as online learning and reinforcement learning allow models to update parameters in real time, responding to changes in data distributions [5]. Continuous monitoring and risk assessment are crucial for identifying model drift, ensuring AI systems remain reliable [97].  
Building models that learn incrementally, updating parameters and structure with new data [124], is effective. Online learning or continual learning techniques allow for adaptation over time without retraining from scratch, preserving existing knowledge while incorporating new insights [122; 123; 124; 125; 126; 127].  
Employing ontology and natural language processing (NLP) techniques aids in dynamically updating domain models, keeping them relevant as knowledge evolves [111]. Ontologies provide structured frameworks for representing domain knowledge, facilitating the integration of new information and relationships [112]. NLP techniques assist in extracting insights from unstructured data sources like research articles and clinical notes, enabling timely knowledge base updates [113]. This is particularly important in fast-paced fields like medicine, where new treatments and guidelines frequently arise.  
Constraint-based models offer a subtle approach to integrating domain knowledge. Instead of explicit rules, these models incorporate constraints or conditions that solutions must adhere to [117; 118]. These constraints, rooted in domain understanding, ensure outputs are statistically valid, practically feasible, and domain-compliant [118; 119]. For instance, in optimization problems, constraints ensure solutions respect physical limitations or resource availability typical to the domain [119; 120].  
Collaborative data understanding involves domain experts working alongside AI systems to curate datasets, addressing biases and ensuring models are trained on relevant, representative data [100] ([PERSON], 2024). This collaboration enhances training data quality, leading to improved model performance and fairness [114] ([PERSON], 2023). Engaging experts in data curation fosters a deeper understanding of model limitations and potential biases, allowing for informed decision-making [115] ([PERSON], 2022). Participatory approaches empower stakeholders, ensuring AI applications align with ethical standards and societal values [116].  
Regular monitoring and evaluation of model performance are crucial. This helps identify when performance degrades due to domain changes and provides insights into domain evolution and model adaptation needs [16; 17].  
In essence, handling dynamic domains requires advanced AI/ML techniques, flexible system design, and ongoing partnerships between models and domain experts. Together, these ensure AI/ML systems remain effective, relevant, and valuable as knowledge landscapes shift and evolve [128; 129; 130; 131].