Department of Bio and Brain Engineering, KAIST, South Korea.
BMC Bioinformatics. 2010 Apr 16;11 Suppl 2(Suppl 2):S3. doi: 10.1186/1471-2105-11-S2-S3.
Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge.
We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships.
We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses.
Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model.
自从 Swanson 提出未被发现的公共知识（UPK）模型以来，已经有许多通过挖掘生物医学文献来发现 UPK 的方法。然而，这些早期的工作需要大量的手动干预来减少可能的连接数量，并且主要应用于疾病-效应关系。随着生物医学科学的进步，从多个不相关的研究、研究和文章中提取和组合信息以推断新的假设和扩展知识变得至关重要。
我们提出了 MKEM，一种多层次知识发现模型，使用自然语言处理技术（如链接语法和本体论，如统一医学语言系统（UMLS）MetaMap）来发现隐含关系。MKEM 的贡献如下：首先，我们提出了一种灵活的知识发现模型，以提取不同层次（如基因和蛋白质的分子水平以及疾病和治疗的表型水平）之间的隐含关系。其次，我们使用 MetaMap 对生物概念进行标记。第三，我们提供了一种发现新关系的经验和系统方法。
我们将我们的系统应用于从 PubMed 数据库下载的 5000 篇摘要。由于尚未提供黄金标准，因此我们进行了性能评估。我们的系统具有良好的精度和召回率，并且生成了 24 个假设。
我们的实验表明，MKEM 是一种强大的工具，可以发现隐藏在我们的物质-效应-过程-疾病-身体部位（SEPDB）模型所表示的提取实体中存在的关系。
