信息系统部知识库

【目的】解决科技大数据数据源分散、质量不高、内容单薄等问题。【方法】采用数据清洗、实体对齐、实体字段融合、冲突检测等增值计算方法, 设计开发一套科技大数据增值丰富化的工具。【结果】通过本文研发的丰富化工具, 在人员、机构、会议、期刊实体及实体关系层面实现实体数据对齐, 实体字段内容增加5-10倍, 实体分析维度提升2-3倍。【局限】增值数据的及时性、规范性需要结合服务需求在实际应用中不断优化提升。【结论】研究成果提升了科技大数据知识发现平台以及相关情报智能分析系统的数据服务维度及深度。

[Objective] This paper tries to address the issues facing sci-tech big data, such as source dispersal, low quality, and poor content. [Methods] We used value-added computing methods, such as data cleansing, entity alignment, entity field fusion, conflict detection, etc., to develop tools for the enrichment of sci-tech big data. [Results] The developed tools achieved entity data alignment at the levels of personnel, organization, conference, journal and relationship among them. The contents of the entity fields were increased by 5 to 10 times, and the entity analysis dimension was increased by 2 to 3 times. [Limitations] The timeliness and standardization of value-added data need to be optimized and improved based on service needs. [Conclusions] The proposed methods and tools enhance the knowledge discovery of the sci-tech big data and intelligent information analysis systems.