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基于大型生物知识网络的OWL推理框架。

OWL reasoning framework over big biological knowledge network.

作者信息

Chen Huajun, Chen Xi, Gu Peiqin, Wu Zhaohui, Yu Tong

机构信息

Department of Computer Science, Zhejiang University, Hangzhou 310027, China.

Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.

出版信息

Biomed Res Int. 2014;2014:272915. doi: 10.1155/2014/272915. Epub 2014 Apr 27.

DOI:10.1155/2014/272915
PMID:24877076
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4022201/
Abstract

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

摘要

近年来，生物学领域产生了海量数据。由于蕴含来自不同学科的领域知识，孤立的生物资源被隐性地联系起来。因此，形成了一个庞大的通用生物知识网络。面对如此海量、各异且相互关联的生物数据，提供一种对大型生物网络进行建模、整合和分析的有效方法成为一项挑战。在本文中，我们提出了一个通用的OWL（网络本体语言）推理框架，以研究生物实体之间的隐性关系。一个涵盖中医和西医的综合生物本体被用于创建生物网络的概念模型。然后，将相应的生物数据作为数据模型整合到生物知识网络中。基于概念模型和数据模型，利用一种可扩展的OWL推理方法从生物网络中推断生物实体之间的潜在关联。在我们的实验中，我们专注于中医与西医之间的关联发现。所推导的关联对于生物学家推动新药研发和中医现代化非常有用。实验结果表明，该系统具有高效性、准确性、可扩展性和有效性。

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