Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in Medicine
<|reference_start|>Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in Medicine: Biomedical knowledge is uniquely complex and structured, requiring distinct reasoning strategies compared to other scientific disciplines like physics or chemistry. Biomedical scientists do not rely on a single approach to reasoning; instead, they use various strategies, including rule-based, prototype-based, and case-based reasoning. This diversity calls for flexible approaches that accommodate multiple reasoning strategies while leveraging in-domain knowledge. We introduce KGARevion, a knowledge graph (KG) based agent designed to address the complexity of knowledge-intensive medical queries. Upon receiving a query, KGARevion generates relevant triplets by using the knowledge base of the LLM. These triplets are then verified against a grounded KG to filter out erroneous information and ensure that only accurate, relevant data contribute to the final answer. Unlike RAG-based models, this multi-step process ensures robustness in reasoning while adapting to different models of medical reasoning. Evaluations on four gold-standard medical QA datasets show that KGARevion improves accuracy by over 5.2%, outperforming 15 models in handling complex medical questions. To test its capabilities, we curated three new medical QA datasets with varying levels of semantic complexity, where KGARevion achieved a 10.4% improvement in accuracy.<|reference_end|>
@article{su2024knowledge,
    title={Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in
  Medicine},
    author={Xiaorui Su, Yibo Wang, Shanghua Gao, Xiaolong Liu, Valentina
  Giunchiglia, Djork-Arn'e Clevert, and Marinka Zitnik},
    journal={arXiv preprint arXiv:2410.04660},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04660},
    primaryClass={cs.AI}
}