Knowledge-based question answering using graph neural networks and contextual language representations
Year: 2026
Type: article
Abstract: This work introduces a novel question answering (QA) framework that integrates commonsense knowledge from ConceptNet with deep contextual embeddings from BERT using a graph neural network for structured reasoning. For each question-answer pair, the system constructs a relevant subgraph from ConceptNet, which is then processed using Graph Attention Network v2 (GATv2) to capture semantic relationships among concepts. In parallel, BERT encodes the question-answer pair to provide contextual language representations. These two representations are fused into a joint embedding that combines structured knowledge with unstructured text understanding, enabling richer inference. Evaluations on the CommonsenseQA and OpenBookQA datasets show accuracy improvements of 82.3% and 86.21%, respectively, surpassing existing leading methods. These results highlight the effectiveness of combining knowledge graphs with language models for complex QA tasks requiring commonsense reasoning.
Source: Scientific Reports
Authors: Mohamed Samir, Naglaa Fathy, Walaa Gad
Institution: Ain Shams University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funder: Ain Shams University
OpenAlex ID: W7124916509