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科技大数据知识图谱构建模型与方法研究
王颖1; 钱力1,2; 谢靖1,2; 常志军1,2; 孔贝贝1
2019
Source Publication数据分析与知识发现
Volume3Issue:3Pages:15-26
Abstract

【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。

Other 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.

Keyword科技大数据 知识图谱 本体 知识抽取
DOI10.11925/infotech.2096-3467.2018.1354.
URL查看原文
Indexed ByCSCD ; CSSCI ; 中文核心期刊要目总览
Language中文
CSCD IDCSCD:6552311
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Document Type期刊论文
Identifierhttp://ir.las.ac.cn/handle/12502/10538
Collection中国科学院文献情报中心(北京)_信息系统部
Affiliation1.中国科学院文献情报中心
2.中国科学院大学
First Author Affilication中国科学院文献情报中心
Recommended Citation
GB/T 7714
王颖,钱力,谢靖,等. 科技大数据知识图谱构建模型与方法研究[J]. 数据分析与知识发现,2019,3(3):15-26.
APA 王颖,钱力,谢靖,常志军,&孔贝贝.(2019).科技大数据知识图谱构建模型与方法研究.数据分析与知识发现,3(3),15-26.
MLA 王颖,et al."科技大数据知识图谱构建模型与方法研究".数据分析与知识发现 3.3(2019):15-26.
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