Integrating Deep Learning and Knowledge Graphs for Natural Language Inference

Natural Language Inference (NLI) deals with the problem of logical entailment between two sentences. It is evidenced that apart from interpretation of words and syntactic structures, use of external relevant knowledge is also required. This paper investigates whether the integration of external knowledge, represented as triplets and extracted from Knowledge Graphs (KGs), can improve the performance of a pretrained language model on NLI tasks. It also investigates how the source of knowledge affects performance. Six benchmark datasets, namely RTE, QNLI, SICK, SNLI, DNLI, and SciTail, were enriched with knowledge extracted from AristoTuple and WordNet, while ConceptNet was additionally used for the SNLI and SciTail datasets. Enrichment was applied only to the training data through a strict relevance-based selection mechanism using paraphrase-MiniLM-L6-v2 embeddings and cosine-similarity filtering, and the resulting datasets were used to fine-tune BERT-base-uncased. Evaluation included method-specific configurations selected through validation-based hyperparameter exploration and a controlled multi-seed setting using the vanilla baseline hyperparameters with matched seeds across baseline and enriched datasets. The best observed validation accuracy was 95.24% on SciTail with AristoTuple, while the highest test accuracy, also on SciTail, was 93.70% with ConceptNet. Across both evaluation settings, the effect of knowledge enrichment varied across datasets and knowledge sources, with the multi-seed analysis further assessing the robustness of these effects under controlled training conditions. Overall, the proposed data-level approach achieved competitive performance compared with more complex knowledge-enhanced NLI methods. A taxonomy of existing NLI approaches is also presented.