【摘要】 【目的】研究从科技大数据中提取结构化知识、构建学术知识网络的模型与方法,支持智能知识服务产品的研发提升精准知识发现能力。【方法】提出科技大数据知识图谱的构建模型和技术架构,在汇聚和融合科技大数据知识资源的基础上,以大数据平台分布式存储和高性能计算为支撑环境,详细设计和实现科研实体知识抽取、实体对齐和关系发现、知识融合与语义丰富化、语义化存储、质量管理等知识图谱构建技术。【结果】构建3亿实体和11亿关系的科技大数据知识图谱,有效支撑科技大数据知识发现平台和"慧科研"智能随身助手的服务。【局限】由于数据的规模和复杂性,知识图谱的质量管理仍需花费大量的人力,实体对齐的准确度也有待于提高。【结论】本文提出的知识图谱建设方案适用于科技大数据的知识管理和深加工,有助于科技知识的有效利用。更多还原

【Abstract】 [Objective] This paper tries to extract information from Sci-Tech big data and build an academic knowledge network, aiming to develop smart knowledge services. [Methods] We proposed an Ontology schema and a framework to contruct knowledge graph based on the distributed storage and high-performance computing of big data platform. The proposed model helped us extract and align research entities for relationship discovery. We also adopted the knowledge merging and enrichment, semantic storage and quality management techniques. [Results] We created a huge knowledge graph including more than 300 million entities and 1.1 billion relations. It also supported knowledge discovery platform and smart personal research assistant apps for scientific big data. [Limitations] More research is needed to improve the quality management of knowledge graph, as well as the precision of entity alignment. [Conclusions] The proposed method improve the knowledge management of scientific and technology big data.更多还原