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GS-CBR-KBQA: Graph-structured case-based reasoning for knowledge base question answering
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AI
DOI：10.1016/j.eswa.2024.125090.png)
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Abstract
En 中文
Knowledge Base Question Answering (KBQA) task is an important research direction in natural language processing. Due to the flexibility and ambiguity of natural language, users' questions often have more complex query types and richer semantic information. To address this issue, this paper proposes the GS-CBRKBQA model, a Case-Based Reasoning model tailored for KBQA to improve the semantic parsing accuracy and interpretability of natural language questions. The model integrates Knowledge-oriented Programming Language (KoPL) reasoning graphs with query information, employing a Graph Auto-Encoder and the RoBERTa pretrained language model for a highly effective case retrieval. This integration leads to a more robust knowledge retrieval and application approach, particularly innovative in capturing the relationships within KoPL graphs. The model addresses explicitly complex questions such as multi-hop reasoning and questions involving intricate entity relationships. Finally, our extensive experiments show that the model performs excellently in accuracy and F1 metrics on benchmark datasets such as WebQSP and ComplexWebQuestions, particularly in complex question-answering. The code of our model is available at https://anonymous.4open. science/r/GS-CBR-KBQA.
Keywords:
Knowledge base question answering
Case-based reasoning
Natural language processing
Deep learning
Large language model
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W