Editorial: Special Issue on Quality Assessment of Knowledge Graphs Dedicated to the Memory of Amrapali Zaveri

摘要

We have recently witnessed a rise in the number and scale of knowledge graphs, including YAGO [7], DBpedia [6], Wikidata [3], EventKG [5], and the Google Knowledge Vault [1]. Knowledge graphs are essentially repositories of graph-based data [4]. They store millions of statements about entities of interest in a domain, for instance, people, places, organizations, and events. They are extensively used in various Artificial Intelligence (AI) contexts, from search and natural language processing to data integration, as a means to add context and depth to machine learning and generate human-readable explanations.