妇产科知识图谱构建研究与实现
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摘要
目的:现有医学知识浩瀚如烟,知识图谱是知识展示比较有效的方法。方法:利用自然语言处理技术,对妇产科教材中的医学知识进行抽取和表示,将妇产科知识存储成结构化的知识图谱,方便专业医学人士查询,也方便对大众进行科普。使用中文分词、命名实体识别、实体分类、关系抽取等技术对教科书文本进行信息抽取。结果:将妇产科教材中的知识转变为知识图谱结构。为后期智能医疗等医疗服务提供了理论基础。结论:以教材为来源,构建领域知识图谱很迅速,能够可视化展示医学信息,让人们快速有效了解医学常识。
Objective: The existing medical knowledge is vast, and knowledge graph is a more effective way to display knowledge. Methods: By the knowledge graph technology to extract and express the medical knowledge in the maternity and obstetrics materials, and stores the knowledge of Obstetrics and Gynecology into a structured knowledge base, which is convenient for the professional medical people to search, and also facilitates the popularization of popular science in the public. Chinese text segmentation, named entity recognition, entity classification and relation extraction are used to extract information from textbook texts. Results: Change the knowledge of teaching materials into structured data. It provides a basis for later intelligent medical and other medical services. Conclusion: Using the source of teaching materials to build a field of knowledge graph is very fast, it can visualize the display of medical information, so that people understand the medical knowledge quickly and effectively.
Objective: The existing medical knowledge is vast, and knowledge graph is a more effective way to display knowledge. Methods: By the knowledge graph technology to extract and express the medical knowledge in the maternity and obstetrics materials, and stores the knowledge of Obstetrics and Gynecology into a structured knowledge base, which is convenient for the professional medical people to search, and also facilitates the popularization of popular science in the public. Chinese text segmentation, named entity recognition, entity classification and relation extraction are used to extract information from textbook texts. Results: Change the knowledge of teaching materials into structured data. It provides a basis for later intelligent medical and other medical services. Conclusion: Using the source of teaching materials to build a field of knowledge graph is very fast, it can visualize the display of medical information, so that people understand the medical knowledge quickly and effectively.
引文
[1]SINGHAL A.Introducing the knowledge graph:things,not strings[EB/OL].2012.https://googleblog.blogspot.com/2012/05/introducingknowledge-graph-things-not.html.
[2]BOLLACKER K,EVANS C,PARITOSHP,et al.Freebase:a collaboratively created graph database for structuring human knowledge[C].Proceedings of the 2008 ACM SIGMODinternational conference on management of data.Vancouver:ACM,2008:1247-1250
[3]BIEGA J,KUZEY E,SUCHANEKFM.Inside YAGO2s:a transparent information extraction architecture[C].Proceedings of the22nd international conference on World Wide Web companion.Rio de Janeiro:International World Wide Web Conferences Steering Committee,2013:325-328.
[4]Wang Zhongyuan,Wang Haixun,Understanding Short Texts,in the Association for Computational Linguistics(ACL)(Tutorial),August 2016.
[5]"Open Graph Overview"(2013).Facebook.https://developers.facebook.com/docs/concepts/opengraph/overview/.
[6]CARLSON A,BETTERIDGE J,KISIELB,et al.Toward an architecture for neverending language learning[C]//Proceedings of the twenty-fourth AAAI Conference on artificial intelligence.Atlanta:AAAI Press,2010:3.
[7]搜狗知立方.http://www.sogou.com/,2012.
[8]百度知心.http://tupu.baidu.com/,2012.
[9]唐伟,洪宇,冯艳卉,等.网页中商品"属性-值"关系的自动抽取方法研究[J].中文信息学报,2013(1).
[10]中国医学院医学信息研究所.知识图谱.http://med.ckcest.cn/knowledgeGraph.jsp.
[11]于彤,刘静,贾李蓉,等.大型中医药知识图谱构建研究[J].中国数字医学,2015,10(3):80-82.
[12]王昊奋.规模知识图谱技术[J].中国计算机学会通讯,2014,10(3):64-68.
[2]BOLLACKER K,EVANS C,PARITOSHP,et al.Freebase:a collaboratively created graph database for structuring human knowledge[C].Proceedings of the 2008 ACM SIGMODinternational conference on management of data.Vancouver:ACM,2008:1247-1250
[3]BIEGA J,KUZEY E,SUCHANEKFM.Inside YAGO2s:a transparent information extraction architecture[C].Proceedings of the22nd international conference on World Wide Web companion.Rio de Janeiro:International World Wide Web Conferences Steering Committee,2013:325-328.
[4]Wang Zhongyuan,Wang Haixun,Understanding Short Texts,in the Association for Computational Linguistics(ACL)(Tutorial),August 2016.
[5]"Open Graph Overview"(2013).Facebook.https://developers.facebook.com/docs/concepts/opengraph/overview/.
[6]CARLSON A,BETTERIDGE J,KISIELB,et al.Toward an architecture for neverending language learning[C]//Proceedings of the twenty-fourth AAAI Conference on artificial intelligence.Atlanta:AAAI Press,2010:3.
[7]搜狗知立方.http://www.sogou.com/,2012.
[8]百度知心.http://tupu.baidu.com/,2012.
[9]唐伟,洪宇,冯艳卉,等.网页中商品"属性-值"关系的自动抽取方法研究[J].中文信息学报,2013(1).
[10]中国医学院医学信息研究所.知识图谱.http://med.ckcest.cn/knowledgeGraph.jsp.
[11]于彤,刘静,贾李蓉,等.大型中医药知识图谱构建研究[J].中国数字医学,2015,10(3):80-82.
[12]王昊奋.规模知识图谱技术[J].中国计算机学会通讯,2014,10(3):64-68.