Diverse data! Diverse schemata?

Semantic web

article

Krzysztof Janowicz¹  Cogan Shimizu²  Pascal Hitzler²  Gengchen Mai³  Shirly Stephen¹  Rui Zhu¹  Ling Cai¹  Lu Zhou²  Mark Schildhauer⁴  Zilong Liu¹  Zhangyu Wang¹  Meilin Shi¹ 

[1] Center for Spatial Studies, University of California;Department of Computer Science, Kansas State University;Department of Computer Science, Stanford University;NCEAS, University of California

关键词: Semantic interoperability;    diverse data;    diverse schema;    ontology;    knowledge graphs;    representation learning;   

DOI  :  10.3233/SW-210453

来源: IOS Press

【 摘 要 】

One of the key value propositions for knowledge graphs and semantic web technologies is fostering semantic interoperability, i.e., integrating data across different themes and domains. But why do we aim at interoperability in the first place? A common answer to this question is that each individual data source only contains partial information about some phenomenon of interest. Consequently, combining multiple diverse datasets provides a more holistic perspective and enables us to answer more complex questions, e.g., those that span between the physical sciences and the social sciences. Interestingly, while these arguments are well established and go by different names, e.g., variety in the realm of big data, we seem less clear about whether the same arguments apply on the level of schemata. Put differently, we want diverse data, but do we also want diverse schemata or a single one to rule them all.

【 授权许可】

Unknown