Lossio-Ventura Juan Antonio, Hogan William, Modave François, Guo Yi, He Zhe, Hicks Amanda, Bian Jiang
Health Outcomes & Policy, College of Medicine, University of Florida, Gainesville, Florida, USA.
School of Information, Florida State University, Tallahassee, Florida, USA.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2017 Nov;2017:1284-1287. doi: 10.1109/BIBM.2017.8217845. Epub 2017 Dec 18.
Obesity has been linked to several types of cancer. Access to adequate health information activates people's participation in managing their own health, which ultimately improves their health outcomes. Nevertheless, the existing online information about the relationship between obesity and cancer is heterogeneous and poorly organized. A formal knowledge representation can help better organize and deliver quality health information. Currently, there are several efforts in the biomedical domain to convert unstructured data to structured data and store them in Semantic Web knowledge bases (KB). In this demo paper, we present, OC-2-KB (Obesity and Cancer to Knowledge Base), a system that is tailored to guide the automatic KB construction for managing obesity and cancer knowledge from free-text scientific literature (i.e., PubMed abstracts) in a systematic way. OC-2-KB has two important modules which perform the acquisition of entities and the extraction then classification of relationships among these entities. We tested the OC-2-KB system on a data set with 23 manually annotated obesity and cancer PubMed abstracts and created a preliminary KB with 765 triples. We conducted a preliminary evaluation on this sample of triples and reported our evaluation results.
肥胖已与多种癌症相关联。获取充足的健康信息能促使人们参与自身健康管理，最终改善健康状况。然而，现有的关于肥胖与癌症关系的在线信息杂乱且缺乏条理。形式化的知识表示有助于更好地组织和提供高质量的健康信息。目前，生物医学领域有多项工作致力于将非结构化数据转换为结构化数据，并存储在语义网知识库（KB）中。在本演示论文中，我们展示了OC-2-KB（肥胖与癌症知识库）系统，该系统旨在以系统的方式指导从自由文本科学文献（即PubMed摘要）中自动构建知识库来管理肥胖与癌症知识。OC-2-KB有两个重要模块，分别执行实体获取以及这些实体之间关系的提取与分类。我们在一个包含23篇人工标注的肥胖与癌症PubMed摘要的数据集上测试了OC-2-KB系统，并创建了一个包含765个三元组的初步知识库。我们对这个三元组样本进行了初步评估并报告了评估结果。
