Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in Medicine

• URL: http://arxiv.org/abs/2410.04660v1
• Date: Mon, 7 Oct 2024 00:17:37 GMT
• Title: Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in Medicine
• Authors: Xiaorui Su, Yibo Wang, Shanghua Gao, Xiaolong Liu, Valentina Giunchiglia, Djork-Arné Clevert, Marinka Zitnik,
• Abstract summary: Biomedical scientists do not rely on a single approach to reasoning. KGARevion is a knowledge graph based agent designed to address the complexity of medical queries.
• Score: 31.080514888803886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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