基于医学文献的主题演化类型与演化路径识别方法研究
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摘要
[目的/意义]利用医学语义关系,基于医学文献开展主题演化类型与演化路径识别方法研究,为进一步丰富和完善主题演化理论与方法提供参考。[方法/过程]首先描述主题演化的新生、消亡、继承、分裂、融合和孤立6种类型,设计了基于统计与语义相结合的关键关联计算方法,提出利用密度和热度开展核心主题识别的研究,从关键关联与核心主题两方面共同识别主题的演化路径,进而确定主题演化类型。[结果/结论]采用本文研究思路与方法,对肝癌致病机制的研究领域文献开展主题演化路径识别与演化类型确定研究,实验证明所提方法可以较好描述领域的主要演化情况。后续研究将改进关键关联构建方法,以提升演化路径识别方法的精度。
[Purpose/significance] In order to enrich and improve topic evolution theories and methods,this paper studies the topic evolution type and method of path identification using medical semantic association based on medical literature.[Method/process] Firstly,the types of topic evolution are described,such as new born,disappearance,inheritance,fission,fusion and isolation.Then the key association calculation method based on the combination of statistics and semantics is designed,and the identification method of core topics using density and hotness is proposed.From the two aspects of key association and core topics,the paper can further identify the topic evolution path and type.[Result/conclusion] The proposed research method is applied in the research documents of liver cancer pathogenesis for topic evolution path identification and evolution type recognition.The experiment proves that the method can better describe the main evolution situation of the field.The follow-up research will improve the key association construction method to enhance the accuracy of the evoluation path recognition method.
[Purpose/significance] In order to enrich and improve topic evolution theories and methods,this paper studies the topic evolution type and method of path identification using medical semantic association based on medical literature.[Method/process] Firstly,the types of topic evolution are described,such as new born,disappearance,inheritance,fission,fusion and isolation.Then the key association calculation method based on the combination of statistics and semantics is designed,and the identification method of core topics using density and hotness is proposed.From the two aspects of key association and core topics,the paper can further identify the topic evolution path and type.[Result/conclusion] The proposed research method is applied in the research documents of liver cancer pathogenesis for topic evolution path identification and evolution type recognition.The experiment proves that the method can better describe the main evolution situation of the field.The follow-up research will improve the key association construction method to enhance the accuracy of the evoluation path recognition method.
引文
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[19] 祝娜,王芳.基于主题关联的知识演化路径识别研究——以3D打印领域为例[J].图书情报工作,2016(5):101-109.
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[21] 黄乾荣,张玲.原发性肝癌治疗研究新进展[J].实用医学杂志,2016(14):2275-2278.
[2] 王莉亚.基于离群数据的主题演化规律分析[J].情报杂志,2013,32(6):59-63.
[3] BARBIERI N,GHISETTI C,GILLI M,et al.A survey of the literature on environmental innovation based on main path analysis[J].Journal of Economic Surveys,2016,30(3):596-623.
[4] 范维熙,费钟琳.基于德温特专利引文网络的技术演进路径研究——以太阳能电池技术为例[J].情报杂志,2014,33(11):62-66.
[5] 陈亮,杨冠灿,张静.面向技术演化分析的多主路径方法研究[J].图书情报工作,2015,59(10):124-130.
[6] 秦晓慧,乐小虬.基于LDA主题关联过滤的领域主题演化研究[J].现代图书情报技术,2015,31(3):18-25.
[7] 李杰,陈超美.citespace:科技文本挖掘及可视化[M].北京:首都经济贸易大学出版社,2016.
[8] MALIK S,SMITH A,HAWES T,et al.TopicFlow:visualizing topic alignment of Twitter data over time[M].IEEE Computer Society,2013.
[9] CUI W,LIU S,TAN L,et al.TextFlow:towards better understanding of evolving topics in text[J].IEEE Transactions on Visualization & Computer Graphics,2011,17(12):2412.
[10] ZHANG L F,YAO Z H,SONG H C,et al.Overview of theme river visualization[J].Journal of System Simulation,2013.
[11] XU L,NAKAYAMA M,WU H Y,et al.TimeTubes:design of a visualization tool for time-dependent,multivariate blazar Datasets[C]//Nicograph International.IEEE,2016:15-20.
[12] 程齐凯,王晓光.一种基于共词网络社区的科研主题演化分析框架[J].图书情报工作,2013,57(8):91-96.
[13] 郝心宁.生物育种领域知识结构与知识演化研究[D].北京:中国农业科学院,2013.
[14] 刘自强,王效岳,白如江.多维度视角下学科主题演化可视化分析方法研究——以我国图书情报领域大数据研究为例[J].中国图书馆学报,2016(6):67-84.
[15] 隗玲,许海云,胡正银,等.学科主题演化路径的多模式识别与预测——一个情报学学科主题演化案例[J].图书情报工作,2016(13):71-81.
[16] 楚克明,李芳.基于LDA模型的新闻话题的演化[J].计算机应用与软件,2011,28(4):4-7,26.
[17] 胡吉明,陈果.基于动态LDA主题模型的内容主题挖掘与演化[J].图书情报工作,2014,58(2):138-142.
[18] 范少萍,安新颖,逯万辉.医学文献主题语义相似度计算方法研究[J].图书情报工作,2017(8):96-105.
[19] 祝娜,王芳.基于主题关联的知识演化路径识别研究——以3D打印领域为例[J].图书情报工作,2016(5):101-109.
[20] 戴维·诺克,杨松.社会网络分析[M].上海:上海人民出版社,2012:103-104.
[21] 黄乾荣,张玲.原发性肝癌治疗研究新进展[J].实用医学杂志,2016(14):2275-2278.
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