计算机科学
图形
知识图
数据挖掘
数据科学
计算生物学
情报检索
理论计算机科学
生物
作者
Henning Schäfer,Ahmad Idrissi-Yaghir,Kamyar Arzideh,Hendrik Damm,Tabea Margareta Grace Pakull,Cynthia S. Schmidt,Mikel Bahn,Georg Lodde,Elisabeth Livingstone,Dirk Schadendorf,Felix Nensa,Péter Horn,Christoph M. Friedrich
标识
DOI:10.1016/j.csbj.2024.10.017
摘要
The growth of biomedical literature presents challenges in extracting and structuring knowledge. Knowledge Graphs (KGs) offer a solution by representing relationships between biomedical entities. However, manual construction of KGs is labor-intensive and time-consuming, highlighting the need for automated methods. This work introduces BioKGrapher, a tool for automatic KG construction using large-scale publication data, with a focus on biomedical concepts related to specific medical conditions. BioKGrapher allows researchers to construct KGs from PubMed IDs.
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