Knowledge enhanced edge-driven graph neural ranking for biomedical information retrieval

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaofeng Liu, Jiajie Tan, Shoubin Dong
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引用次数: 0

Abstract

Neural networks used for information retrieval tend to capture textual matching signals between a query and a document. However, neural ranking models for biomedical information retrieval often struggle to semantically well match the query to the documents. The main reasons are that biomedical terms have many different representations and the fact description related to the query is non-consecutive and non-local in the documents. In this paper, we present an edge-driven graph neural ranking method for biomedical information retrieval by incorporating knowledge from medical databases. First, we propose to form an edge-driven graph by connecting some biomedical terms in the query and the document through different types of edges. Then, we design a novel way of knowledge integration to introduce knowledge related to biomedical terms into the graph and construct a knowledge-query-doc graph. Finally, a graph neural ranking model is applied to capture non-local and non-contiguous match signals between the query and the document. Experimental results show on the biomedical datasets that our method outperforms the advanced neural models. And further analysis shows that the knowledge integration method can well reduce the semantic gap between the query and the document, and our graph model can provide interpretation for matching between the query and the document.
用于生物医学信息检索的知识增强型边缘驱动图神经排序
用于信息检索的神经网络倾向于捕捉查询和文档之间的文本匹配信号。然而,用于生物医学信息检索的神经排序模型往往难以从语义上很好地匹配查询和文档。主要原因是生物医学术语有许多不同的表示方法,而且与查询相关的事实描述在文档中是非连续和非局部的。本文结合医学数据库知识,提出了一种用于生物医学信息检索的边缘驱动图神经排序方法。首先,我们提出将查询和文档中的一些生物医学术语通过不同类型的边连接起来,形成一个边驱动图。然后,我们设计了一种新颖的知识整合方式,将与生物医学术语相关的知识引入图中,构建了一个知识-查询-文档图。最后,我们应用图神经排序模型来捕捉查询和文档之间的非局部和非连续匹配信号。生物医学数据集的实验结果表明,我们的方法优于先进的神经模型。进一步的分析表明,知识整合方法可以很好地缩小查询和文档之间的语义差距,而我们的图模型可以为查询和文档之间的匹配提供解释。
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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