一种简历语义搜索系统的实现方法  

SmartHR:A Resume Query and Management System Based on Semantic Web

作　　者：柯叶青[1,2] 马志柔[2] 伍海江 刘杰[2] 

机构地区：[1]中国科学院大学计算机与控制学院,北京100190 [2]中国科学院软件研究所,北京100190

出　　处：《计算机科学》2015年第12期56-59,共4页Computer Science

基　　金：国家自然科学基金项目(61170074;61202065);国家高技术研究发展计划(2012AA011204)资助

摘　　要：政府与企事业单位的人事部门通常面临着如何从大量简历中筛选出合适人选的问题。一些业务部门对人才的需求通常只能表达为标签,比如"具有丰富搜索引擎开发经验"、"985高校毕业"等,这些需求不能通过SQL查询或关键词搜索来实现。为了解决这个问题,提出一种基于语义网的简历搜索方法。通过领域知识库辅助信息提取的方法,对简历信息进行语义分析和标签的自动生成。此外,在大规模人员情况下,提出了多层次缓存的方法,极大提高了性能。将该方法应用于某机构一万人员简历的筛选,实验结果表明了其有效性。The personnel departments of organizations are always confronted with the challenge of efficiently finding out suitable candidates from massive resumes. Some business departments usually express their demand for talents as tags, such as ＂with rich search engine development experience＂, ＂graduated from 985 universities＂ and so on. These requirements cannot be done by SQL queries or keyword search. To fill this gap, this paper proposed a resume analysis and search method based on semantic Web. By the method of domain knowledge base auxiliary information extraction, resume information is semantically analyzed and labels for suitable candidates are automatically generated. In addition, on a large personnel scale,we proposed a method of multi-level cache by which the performance has been greatly improved. Meanwhile, it is applied to nearly ten thousand personnel agency resumes and experiments show the effectiveness.

关 键 词：语义网 RDF SPARQL 性能优化 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]