| 科技资源元数据的关联与推荐方法 |
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| 引用本文： | 宋,佳,高少华,杨,杰,诸云强.科技资源元数据的关联与推荐方法[J].中国信息导报,2017(5):37-44. |
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| 作者姓名： | 宋 佳 高少华 杨 杰 诸云强 |
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| 作者单位： | 1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京 100101；
2. 武汉大学资源与环境科学学院，湖北武汉 430079；3. 中国科学院大学，北京 100049；
4. 江苏省地理信息资源开发与利用协同创新中心，江苏南京 210023；5. 白洋淀流域生态保护与
京津冀可持续发展协同创新中心，河北保定 071002 |
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| 基金项目： | 科技基础性工作专项项目“科技基础性工作数据资料集成与规范化整编”（2013FY110900）；国家自然科学基金重点项
目“网络文本蕴含信息理解与知识图构建”（41631177）。 |
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| 摘 要： | 大数据背景下，科技资源发现和推荐的关键是建立海量、多类型科技资源间的关联，并对其进行相关度排
序。在深入研究科技基础性工作专项科技资源核心元数据的基础上，选择科技资源的内容特征、资源地点和资源时间
为关联要素。然后结合专家打分和层次分析法，提出了科技资源元数据语义相关度算法，建立了科技资源间的关联。
进一步按照相关度计算结果对科技资源进行排序，并将相关度高的科技资源优先推荐给用户。最后以科技基础性工作
专项项目汇交的科技资源元数据为例，开展了科技资源元数据关联与推荐的实践。本研究提出的方法为促进海量科技
资源的精准发现、智能推荐与共享应用提供了借鉴。
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| 关 键 词： | 科技资源 元数据 语义关联 语义相关度 |
| 收稿时间： | 2017/7/14 0:00:00 |
Association and Recommendation Method for Metadata of Scientific and Technical Resources |
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| SONG Ji,GAO Shaohu,YANG Jie,ZHU Yunqiang.Association and Recommendation Method for Metadata of Scientific and Technical Resources[J].China Information Review,2017(5):37-44. |
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| Authors: | SONG Ji GAO Shaohu YANG Jie ZHU Yunqiang |
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| Institution: | 1.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic
Sciences and Natural Resources Research, CAS, Beijing 100101;2.School of Resource and Environment
Science, Wuhan University, Wuhan, Hubei 430079;3.University of Chinese Academy of Sciences, Beijing
100049;4.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development
and Application, Nanjing, Jiangsu 210023;5.Collaborative Innovation Centre for Baiyangdian Basin Ecological
Protection and Jingjinji Regional Sustainable Development, Hebei University, Baoding, Hebei, 071002 |
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| Abstract: | In the context of big data, efficient discovery and recommendation for scientific and technical data
resources is to build the association between these data resources and then sort them by relevancy. Based on
the investigation of core metadata of National Special Program on Basic Works for Science and Technology of
China, this study chooses the content, location and temporal information of the data resources as association factors. Then, a semantic relevance algorithm is proposed based on the method of expert scoring and analytic
hierarchy process, and the semantic association between these data resources is achieved in this study. These
data resources are able to be sorted in terms of the semantic relevance, and the data resources with high
relevance value can be recommended to the users. The proposed method is validated in the application
case of data archiving and sharing for the projects of National Special Program on Basic Works for Science
and Technology of China, and it has great significance in promoting the accurate discovery, intelligent
recommendation and sharing for scientific data. |
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| Keywords: | scientific and technical resources metadata semantic association semantic relevance |
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