Abstract

In the past decade, systems that extract information from millions of Internet documents have become commonplace. Knowledge graphs–structured knowledge bases that describe entities, their attributes and the relationships between them–are a powerful tool for understanding and organizing this vast amount of information. However, a significant obstacle to knowledge graph construction is the unreliability of the extracted information, due to noise and ambiguity in the underlying data or errors made by the extraction system and the complexity of reasoning about the dependencies between these noisy extractions. My dissertation addresses these challenges by exploiting the interdependencies between facts to improve the quality of the knowledge graph in a scalable framework. I introduce a new approach called knowledge graph identification (KGI), which resolves the entities, attributes and relationships in the …

Date

2016

Authors

Jay Pujara

Institution

University of Maryland, College Park