多源异构数据情境中学术知识图谱模型构建研究

Research on the Construction of Academic Knowledge GraphModel in Multi-source Heterogeneous Data Situation

[目的/意义]随着大数据和人工智能技术的蓬勃发展,数据驱动的智慧学术研究以及基于学术大数据的知识发现受到产业界和学术界的广泛关注。学术知识图谱是学术信息挖掘和学术知识管理的基础,在智慧学术研究中具有重要的学术价值和产业价值。[方法/过程]本文以构建智慧学术服务的实际需求为出发点,从学术大数据的获取、学术实体识别、学术实体链接与知识融合、学术知识图谱本体模型构建、学术知识图谱表示与存储等核心问题入手,提出智慧学术领域的知识图谱构建的理论模型。[结论/结果]多源异构数据融合的学术知识图谱是支撑智慧学术的数据基础,同时也是人工智能及知识表示技术在学术大数据领域的重要应用。

[Purpose/Significance]With the rapid development of big data and artificial intelligence technology,data-driven intelligent academic research and knowledge discovery based on academic big data have received extensive attention from industry and academic.Academic knowledge graph is the foundation of academic information mining and academic knowledge management,and has important academic value and industrial value in intelligent academic research.[Method/Process]This paper started from the actual needs of building intelligent academics service,begining with the core issues of academic big data acquisition,academic entity identification,academic entity link and knowledge fusion,academic knowledge map ontology model construction,academic knowledge graph representation and storage,and proposed the theoretical model for the construction of knowledge graph in the field of smart academics.[Result/Conclusion]The construction of academic knowledge graph for multi-source heterogeneous data fusion was the data foundation supporting intelligent academics,and also an important application of artificial intelligence and knowledge representation technology in the field of academic big data.