KIMedQA: towards building knowledge-enhanced medical QA models

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Aizan Zafar, Sovan Kumar Sahoo, Deeksha Varshney, Amitava Das, Asif Ekbal
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引用次数: 0

Abstract

Medical question-answering systems require the ability to extract accurate, concise, and comprehensive answers. They will better comprehend the complex text and produce helpful answers if they can reason on the explicit constraints described in the question’s textual context and the implicit, pertinent knowledge of the medical world. Integrating Knowledge Graphs (KG) with Language Models (LMs) is a common approach to incorporating structured information sources. However, effectively combining and reasoning over KG representations and language context remains an open question. To address this, we propose the Knowledge Infused Medical Question Answering system (KIMedQA), which employs two techniques viz. relevant knowledge graph selection and pruning of the large-scale graph to handle Vector Space Inconsistent (VSI) and Excessive Knowledge Information (EKI). The representation of the query and context are then combined with the pruned knowledge network using a pre-trained language model to generate an informed answer. Finally, we demonstrate through in-depth empirical evaluation that our suggested strategy provides cutting-edge outcomes on two benchmark datasets, namely MASH-QA and COVID-QA. We also compared our results to ChatGPT, a robust and very powerful generative model, and discovered that our model outperforms ChatGPT according to the F1 Score and human evaluation metrics such as adequacy.

Abstract Image

KIMedQA:建立知识增强型医疗质量保证模型
医学问题解答系统需要能够提取准确、简洁和全面的答案。如果它们能根据问题文本上下文中描述的显式限制条件和医学界的隐式相关知识进行推理,就能更好地理解复杂文本并生成有用的答案。将知识图谱(KG)与语言模型(LMs)相结合是整合结构化信息源的常用方法。然而,如何有效地将知识图谱表示和语言上下文结合起来并进行推理,仍然是一个有待解决的问题。为了解决这个问题,我们提出了知识注入式医学问题解答系统(KIMedQA),该系统采用了两种技术,即相关知识图谱选择和大规模图谱修剪,以处理矢量空间不一致(VSI)和知识信息过多(EKI)问题。然后,利用预先训练好的语言模型,将查询和上下文的表示与剪枝后的知识网络相结合,生成有依据的答案。最后,我们通过深入的实证评估证明,我们建议的策略在两个基准数据集(即 MASH-QA 和 COVID-QA)上提供了最先进的结果。我们还将结果与强大的生成模型 ChatGPT 进行了比较,发现根据 F1 分数和人类评估指标(如充分性),我们的模型优于 ChatGPT。
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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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