Background/Objectives: Drug–drug interactions (DDIs) are major medication-safety concerns, and experimental testing cannot cover the expanding number of drug pairs. This systematic review evaluates graph-based machine-learning methods for DDI prediction, focusing on machine-learning architectures, data integration, interpretability, reproducibility, and clinical relevance. Methods: Following PRISMA 2020, we systematically searched major databases for studies published between January 2021 and March 2026. We included studies that applied graph-based machine-learning models, particularly graph neural networks, to predict DDIs. We compared their data sources, model designs, validation methods, predictive performance, reproducibility, and clinical relevance. Because the studies used different datasets and evaluation methods, the findings were summarized narratively rather than combined statistically. Results: Sixty studies met the eligibility criteria. Methods progressed from graph convolutional networks and graph attention networks to graph transformers, contrastive learning, multimodal fusion, and LLM-enhanced representations. We found that reported improvements in prediction performance often remained study-specific. Only three studies explicitly mentioned or addressed data leakage, whereas most reviewed studies contained no explicit leakage discussion; leakage-aware drug-disjoint, temporal, and external evaluations were also uncommon. Uncertainty calibration, computational-resource reporting, complete reproducibility materials, and independently validated explanations were also limited. Conclusions: Graph-based machine learning is promising for DDI prioritization and hypothesis generation but remains insufficient for independent clinical decision-making. Future studies should use standardized benchmarks, leakage-aware validation, calibrated uncertainty, reproducible pipelines, validated explanations, and external or prospective evaluation.

This paper presents an ontology developed to address challenges such as com munication gaps, risks of errors, and inconsistencies during the manual process of creating software requirement specifications (SRS). The proposed ontology offers a systematic and formal depiction of the requirements, enhancing consis tency and communication among stakeholders. The ontology has been devel oped from the software requirements documents to facilitate the development process. This paper discusses the process of creating the ontology and demon strates using Pellet Reasoner for inference and Prot´eg´e for ontology construction to save and reuse information. The ontology seems to be efficient in manag ing complex software projects, enabling accurate requirement retrieval through SPARQL queries. This study emphasizes how incorporating ontologies into re quirement engineering can significantly enhance the quality and reliability of SRS.