Knowledge-based question answering using graph neural networks and contextual language representations.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Knowledge Representation

Background:

• Current question answering (QA) systems struggle with commonsense reasoning.
• Integrating structured knowledge with deep learning models is a key challenge.

Purpose of the Study:

• To develop a novel QA framework that leverages commonsense knowledge graphs and deep contextual embeddings.
• To enhance reasoning capabilities in QA systems by combining structured and unstructured data.

Main Methods:

• A graph neural network, specifically Graph Attention Network v2 (GATv2), was employed to process subgraphs from ConceptNet.
• BERT was utilized to generate deep contextual embeddings for question-answer pairs.
• A fusion mechanism combined graph-based structured knowledge with BERT's language representations.

Main Results:

• The framework achieved 82.3% accuracy on CommonsenseQA and 86.21% on OpenBookQA.
• Performance surpassed existing state-of-the-art methods on these commonsense reasoning benchmarks.
• Demonstrated the efficacy of integrating knowledge graphs with language models.

Conclusions:

• The proposed framework effectively combines structured commonsense knowledge with language model understanding.
• This hybrid approach significantly advances QA capabilities, particularly for tasks requiring nuanced reasoning.
• Future work can explore further integration of diverse knowledge sources.