  Entity resolution, the problem of identifying the underlying entity of
references found in data, has been researched for many decades in many
communities. A common theme in this research has been the importance of
incorporating relational features into the resolution process. Relational
entity resolution is particularly important in knowledge graphs (KGs), which
have a regular structure capturing entities and their interrelationships. We
identify three major problems in KG entity resolution: (1) intra-KG reference
ambiguity; (2) inter-KG reference ambiguity; and (3) ambiguity when extending
KGs with new facts. We implement a framework that generalizes across these
three settings and exploits this regular structure of KGs. Our framework has
many advantages over custom solutions widely deployed in industry, including
collective inference, scalability, and interpretability. We apply our framework
to two real-world KG entity resolution problems, ambiguity in NELL and merging
data from Freebase and MusicBrainz, demonstrating the importance of relational
features.
