Semantic Consistency Enforcement Algorithms For Knowledge Graph Augmented Learning

Abstract

Knowledge Graph Augmented Learning (KGAL) has emerged as a transformative paradigm that enhances machine learning models with structured, relational background knowledge. However, a fundamental challenge persists: knowledge graphs frequently contain semantic inconsistencies contradictory triples, ontological violations, and ambiguous entity relationships that propagate errors into downstream learning tasks and degrade model reliability. This paper presents a comprehensive algorithmic framework termed the Semantic Consistency Enforcement Algorithm (SCEA), designed to detect, quantify, and resolve semantic inconsistencies in knowledge graphs prior to and during the learning phase. SCEA integrates ontology-driven constraint checking, triple-level consistency scoring, and an iterative repair mechanism powered by graph neural network embeddings. Empirical evaluation on the FB15k-237 and WN18RR benchmarks demonstrates that SCEA achieves an accuracy of 91.5% and a semantic coherence index of 0.98, reducing inconsistency rates by up to 85.8% compared to unfiltered baselines. The proposed framework establishes a principled methodology for enforcing semantic integrity in knowledge-augmented learning pipelines, with implications for question answering, biomedical reasoning, and enterprise knowledge management.