Addressing Label Sparsity With Class-Level Common Sense for Google Maps.

ABSTRACT: Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting the previous expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on logic-based methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple yet novel three-tier crowd approach to acquiring class-level attributes that represent broad common sense associations between categories, and can be used with the classic knowledge-base default & override

SUBMITTER: Welty C 

PROVIDER: S-EPMC8967349 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature