Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Record linkage advance is key in these disciplines since it allows to increase the volume and the data complexity to be analyzed. However, current methods are constrained to link data coming from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.
In this paper we propose the knowledge graph use to tackle record linkage task. The proposed method, named {\bf WERL}, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.
Demographic Aware Probabilistic Medical Knowledge Graph Embeddings of Electronic Medical Records
University of Bonn（波恩大学）
由电子病历构建的医学知识图谱包含了丰富的患者和医疗实体信息。KG嵌入模型在这些数据上的利用已被证明对于不同的医疗任务是有效的。然而，现有的模型没有适当地纳入患者的人口统计数据，他们中的大多数忽略了医疗KG的概率特征。在本文中，我们提出了DARLING（人口统计学感知pRobabiListic medIcal kNowledge embeddinG），一个人口统计学感知的医疗KG嵌入框架，通过将患者人口统计数据与相应的超平面相关联，明确地将人口统计数据纳入医疗实体空间。我们的框架利用医疗实体内的概率特征，通过人口统计学指导来学习它们的表示。我们评估DARLING通过链接预测的治疗和药物，从EMR数据构建的医疗KG，并说明其优越的性能相比，现有的KG嵌入模型。
FBS Model-based Maintenance Record Accumulation for Failure-Cause Inference in Manufacturing Systems
Department of Precision Engineering, School of Engineering（精密工程系）；Research into Artifacts Center for Engineering (RACE)（工程制品研究中心）；DENSO CORPORATION（DENSO公司）
Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning
Tongji University（同济大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；East China Normal University（华东师范大学）；Stanford University（斯坦福大学）；New York University（纽约大学）
本文提出 VaLiK 方法，通过跨模态信息补充构建无需注释的多模态知识图谱，提升大语言模型推理能力。
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context
College of Information and Computer Science, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校信息与计算机科学学院）；Department of Computer Science, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校计算机科学系）；Department of Medicine, University of Massachusetts Medical School（马萨诸塞大学医学院医学系）；Center for Healthcare Organization and Implementation Research, Bedford Veterans Affairs Medical Center（贝德福德退伍军人事务医疗中心医疗保健组织与实施研究中心）
