KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences.
KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences.
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DOI：
10.1186/s12859-015-0549-5
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发表时间：
2015-05-14
影响因子：
3
通讯作者：
Weikum G
中科院分区：
文献类型：
--
作者：
Ernst P;Siu A;Weikum G
生物医学知识库已成为生命科学的重要资产。以前关于知识库构建的工作有三个主要限制。首先，大多数生物医学知识库都是人工建立和管理的，跟不上新发现的发布速度。其次，对于自动信息提取(IE)，选择的文本体裁一直是科学出版物，而忽略了健康门户网站和在线社区等来源。第三，以前关于IE的大多数工作都集中在分子水平或化学基因组学上，如蛋白质-蛋白质相互作用或基因-药物关系，或仅针对高度特定的主题，如药物效应。我们通过一种通用的、可伸缩的自动知识库构建方法解决了这三个限制。使用少量的种子事实来远程监督基于模式的提取，我们以自动的方式收获了大量的事实，而不需要任何显式的训练。我们使用置信度统计对已有的基于模式的IE技术进行了扩展，并将面向召回的阶段与用于一致性约束检查的逻辑推理相结合，以达到较高的精度。据我们所知，这是第一种对生物医学关系进行一致性检查的方法。我们的方法可以很容易地扩展以包含其他关系和约束。我们不仅对科学出版物进行了广泛的实验，还对百科全书式的健康门户和在线社区进行了广泛的实验，根据不同的配置创建了不同的知识库。我们根据事实的数量和精确度来评估每个知识库的大小和质量。配置最好的知识库KnowLife包含50多万个事实，精确度为93%，涵盖了基因、器官、疾病、症状、治疗以及环境和生活方式风险因素等13个关系。KnowLife是一个关于健康和生命科学的大型知识库，从不同的Web来源自动构建。作为一个独特的功能，KnowLife从不同的文本体裁中收获，如科学出版物、健康门户网站和在线社区。因此，它有可能成为各种关系和用例的一站式门户。为了展示其广泛性和实用性，我们通过健康门户(http://knowlife.mpi-inf.mpg.de).)访问了KnowLife知识库本文的在线版本(doi：10.1186/s12859-0150549-5)包含补充材料，授权用户可以使用。
Biomedical knowledge bases (KB’s) have become important assets in life sciences. Prior work on KB construction has three major limitations. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects. We address these three limitations by a versatile and scalable approach to automatic KB construction. Using a small number of seed facts for distant supervision of pattern-based extraction, we harvest a huge number of facts in an automated manner without requiring any explicit training. We extend previous techniques for pattern-based IE with confidence statistics, and we combine this recall-oriented stage with logical reasoning for consistency constraint checking to achieve high precision. To our knowledge, this is the first method that uses consistency checking for biomedical relations. Our approach can be easily extended to incorporate additional relations and constraints. We ran extensive experiments not only for scientific publications, but also for encyclopedic health portals and online communities, creating different KB’s based on different configurations. We assess the size and quality of each KB, in terms of number of facts and precision. The best configured KB, KnowLife, contains more than 500,000 facts at a precision of 93% for 13 relations covering genes, organs, diseases, symptoms, treatments, as well as environmental and lifestyle risk factors. KnowLife is a large knowledge base for health and life sciences, automatically constructed from different Web sources. As a unique feature, KnowLife is harvested from different text genres such as scientific publications, health portals, and online communities. Thus, it has the potential to serve as one-stop portal for a wide range of relations and use cases. To showcase the breadth and usefulness, we make the KnowLife KB accessible through the health portal (http://knowlife.mpi-inf.mpg.de). The online version of this article (doi:10.1186/s12859-015-0549-5) contains supplementary material, which is available to authorized users.
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影响因子：
1.1
作者：
JOHNSON, DS
通讯作者：
JOHNSON, DS
影响因子：
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作者：
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通讯作者：
Tsujii J
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Krallinger M;Izarzugaza JM;Rodriguez-Penagos C;Valencia A
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影响因子：
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Cohen KB