PubMed摘要中组织社交网络的自动提取
Towards Automatic Extraction of Social Networks of Organizations in PubMed Abstracts
摘要：
组织的社会网络分析（SNA）可以吸引政府机构和科学家的极大兴趣，因为它能够促进转化研究并加速将研究转化为护理的过程。对于特定疾病领域的SNA，我们需要通过从PubMed中挖掘隶属关系信息来确定该领域的关键研究组。这不仅涉及识别隶属关系字符串中的组织名称，还涉及解决歧义以识别具有唯一组织的文章。我们在这里提出了一个规范化的过程，涉及聚类的基础上，本地序列比对指标和本地学习的基础上找到连接的组件。我们通过分析参与血管生成治疗的组织来展示该方法的应用，并展示该结果对制药和生物技术行业或国家资助机构的研究人员的实用性。
英文摘要：
Social Network Analysis (SNA) of organizations can attract great interest from government agencies and scientists for its ability to boost translational research and accelerate the process of converting research to care. For SNA of a particular disease area, we need to identify the key research groups in that area by mining the affiliation information from PubMed. This not only involves recognizing the organization names in the affiliation string, but also resolving ambiguities to identify the article with a unique organization. We present here a process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. We demonstrate the application of the method by analyzing organizations involved in angiogenensis treatment, and demonstrating the utility of the results for researchers in the pharmaceutical and biotechnology industries or national funding agencies.
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