Abstract

Natural Language Understanding (NLU) faces both opportunities and challenges as the amount of social media and healthcare data grows. This is particularly evident in context-sensitive applications such as evaluating cognitive health, identifying mental health symptoms, and monitoring drug abuse. Even though traditional NLU models work well for processing language in a wide range of areas, they often lack the ability to understand language in a specific domain, reason in context, and incorporate structured external knowledge. This dissertation talks about the Knowledge and Ontology Enhanced Approach to Natural Language Understanding (KOE-NLU), a new framework that is meant to make NLU systems better at understanding semantic depth, contextual awareness, and drawing conclusions in computational social media and healthcare informatics. The KOE-NLU framework is built upon three fundamental pillars: There are three types of methods. The first is Ontology-Driven Semantic Representation, which puts domain-specific knowledge into formalized ontologies to help with making sense of things based on their context and drawing conclusions from them. The second is Knowledge Graph Integration, which links different types of data sources to improve structured representation and reasoning over unstructured text. The third type is transformer-based language models with knowledge augmentation. This type of modeling uses domain-specific prompts to teach transformer based models, generative AI models structured knowledge sources. These components work in synergy to enable more accurate and context-aware natural language predictions. To demonstrate the efficacy of the KOE-NLU framework, this dissertation presents four major application areas. In the first place, the framework is used in cognitive health informatics to predict Mild Cognitive Impairment (MCI) by putting together MoCA test scores and automated discourse analysis. This is done by using a Cross-Cognitive Domain Attention (CCDA) model to pull out linguistic markers that show cognitive decline. Second, we present process knowledge-guided language prompting as a way to automate Main Concept Analysis (MCA) for discourse assessment. It is shown that process-aware language models are better than traditional text-based classifiers at detecting speech coherence in people with neurodegenerative conditions. Third, in mental health analytics, a knowledge-infused multi-task learning framework is developed to extract mental health symptoms linked to cardiovascular disease (CVD) from social media discourse, employing hierarchical attention networks combined with expert-curated knowledge bases. Finally, the dissertation introduces a Drug Abuse Ontology (DAO) that was created using semi-automated ontology engineering methods. We can use this ontology to identify patterns in substance use disorders, monitor illicit drug trends on social media, and examine the evolution of drug-related emotions over time. A rigorous experimental framework is implemented to evaluate the KOE-NLU models across these applications. The role of ontology-enhanced reasoning in model performance is looked at by comparing supervised and self-supervised learning paradigms. The results show that knowledge-infused transformer architectures do better than baseline deep learning models by as much as 17% in the F1 score and are better at interpreting clinical discourse in terms of context. Structured ontological constraints also make it much easier to classify substance use disorders and pull out mental health symptoms. This cuts down on false positives in automated detection systems by over 12%. This dissertation makes computational social media analytics and healthcare informatics better by giving us a knowledge-driven NLU framework that can be used on a large scale. This framework connects data-driven machine learning with symbolic AI methods. The KOE-NLU framework creates the foundation for the next generation of AI models that can be explained. These models will use process knowledge, ontological reasoning, and knowledge graphs to help computers understand language better in important healthcare settings. More work can be built on top of this by including speech, physiological, and neuroimaging signals, making domain-adaptive self-learning ontologies, and setting up real-time clinical decision support systems. This research marks a significant advancement in KOE-NLU, with important real-world applications in healthcare AI, clinical decision support, and public health monitoring on social media.