基于知识张量分解的BI-RADS知识图的换能化学习

Jianing Xi, Zhaoji Miao, Qinghua Huang
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引用次数: 2

摘要

知识图谱的优势极大地提高了人工智能诊断的可解释性。对于乳腺超声,可以通过BI-RADS语义描述来构建KG,通过重建患者与结果之间的链接来实现诊断。然而,现有的KG分析方法在嵌入时只考虑实体和关系的链接邻居,而不是KG中的整个实体和关系,这降低了仅对一小部分标记患者进行诊断的链接重建能力。本文提出了一种基于换向学习的知识张量分解(Knowledge Tensor Factorization, KTF)方法,该方法通过所有实体和关系及其嵌入向量之间的交互核心张量来有效地表示KG数据。即使只有一小部分被标记的患者,KTF也显示出明显的诊断性能。通过评估实验,KTF在诊断一小部分已知预后的BI-RADS KG数据方面表现出明显的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transductive Learning for BI-RADS Knowledge Graph based on Knowledge Tensor Factorization
The advantage of Knowledge Graph (KG) can greatly prompt the interpretability of the artificial intelligence diagnosis. For breast ultrasound, the KG can be built through BI-RADS semantic descriptions, and the diagnosis can be achieved by link reconstruction between patients and outcomes. However, the existing KG analysis methods consider only the linked neighbors of the entities and relations during embedding, but not the whole entities and relations in KG, which reduces the link reconstruction power for diagnosis in the case of only a small fraction of labeled patients. In this paper, we present a transductive learning based Knowledge Tensor Factorization (KTF) method, which can effectively represent the KG data through a core tensor of interactions among all entities and relations and their embedding vectors. KTF demonstrates distinct diagnosis performance even if there is only a small fraction of labeled patients. Through experiments of assessments, KTF shows distinct superior performance in diagnosis for KG data of BI-RADS with a small fraction of known outcomes of patients.
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