Ontology-Driven User Intent Prediction in Conversational Interfaces Using Semantic Enrichment Pipelines

Abstract

The accurate prediction of user intent in conversational interfaces remains a fundamental challenge in natural language understanding, particularly when utterances are ambiguous, context-dependent, or domain-specific. Conventional intent classification approaches rely predominantly on statistical co-occurrence patterns and surface-level lexical features, rendering them brittle in the face of semantic variation and knowledge gaps. This paper presents a novel framework that integrates formal ontological structures with semantic enrichment pipelines to substantially improve intent prediction accuracy in dialogue systems.

The proposed architecture leverages domain ontologies to inject structured background knowledge into the intent recognition process, enabling the system to resolve lexical ambiguity, infer implicit user goals, and generalize across semantically related expressions. By aligning user utterances with ontological concepts through a multi-stage enrichment pipeline—comprising entity linking, concept expansion, and relation-aware encoding—the framework produces semantically grounded representations that transcend the limitations of purely data-driven methods.

Empirical evaluation across three benchmark conversational datasets demonstrates that ontology-augmented models achieve statistically significant improvements over state-of-the-art baselines, yielding gains of up to 8.3\% in macro-averaged F1-score and 11.2\% reduction in out-of-vocabulary intent misclassification. Ablation studies further confirm that each constituent stage of the semantic enrichment pipeline contributes independently and cumulatively to overall predictive performance.

The findings establish that principled integration of symbolic knowledge representations with neural language models constitutes a robust and interpretable strategy for intent understanding in conversational AI. This work contributes both a reproducible experimental methodology and a publicly available ontology-enriched evaluation benchmark, advancing the broader agenda of knowledge-informed natural language processing in human-computer interaction systems.