计算机科学
元数据
知识图
可扩展性
图形
构造(python库)
情报检索
数据科学
理论计算机科学
万维网
数据库
程序设计语言
作者
Gleb Gawriljuk,Andreas Harth,Craig A. Knoblock,Pedro Szekely
标识
DOI:10.1007/978-3-319-43997-6_15
摘要
We work on converting the metadata of 13 American art museums and archives into Linked Data, to be able to integrate and query the resulting data. While there are many good sources of artist data, no single source covers all artists. We thus address the challenge of building a comprehensive knowledge graph of artists that we can then use to link the data from each of the individual museums. We present a framework to construct and incrementally extend a knowledge graph, describe and evaluate techniques for efficiently building knowledge graphs through the use of the MinHash/LSH algorithm for generating candidate matches, and conduct an evaluation that demonstrates our approach can efficiently and accurately build a knowledge graph about artists.
科研通智能强力驱动
Strongly Powered by AbleSci AI