Abstract
In the context of big data,many large-scale know-ledge graphs have emerged to effectively organize the explosive growth of web data on the Internet.To select suitable know-ledge graphs for use from many knowledge graphs,quality assessment is particularly important.As an important thing of quality assessment,completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph,it is often necessary to refine the completeness dimension by set-ting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However,lack of awareness of requirements is the most prob-lematic quality issue.In the actual evaluation process,the exist-ing completeness metrics need to consider the actual applica-tion.Therefore,to accurately recommend suitable knowledge graphs to many users,it is particularly important to develop rele-vant measurement metrics and formulate measurement schemes for completeness.In this paper,we will first clarify the concept of completeness,establish each metric of completeness,and finally design a measurement proposal for the completeness of knowledge graphs.
基金项目
National Key Laboratory for Complex Systems Simulation Foundation(6142006190301)
NETL
NSTL
万方数据